U.S. patent application number 16/454217 was filed with the patent office on 2020-01-02 for systems and methods for dynamic route planning in autonomous navigation.
The applicant listed for this patent is Brain Corporation. Invention is credited to Borja Ibarz Gabardos, Jean-Baptiste Passot.
Application Number | 20200004253 16/454217 |
Document ID | / |
Family ID | 62021299 |
Filed Date | 2020-01-02 |
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United States Patent
Application |
20200004253 |
Kind Code |
A1 |
Gabardos; Borja Ibarz ; et
al. |
January 2, 2020 |
SYSTEMS AND METHODS FOR DYNAMIC ROUTE PLANNING IN AUTONOMOUS
NAVIGATION
Abstract
Systems and methods for dynamic route planning in autonomous
navigation are disclosed. In some exemplary implementations, a
robot can have one or more sensors configured to collect data about
an environment including detected points on one or more objects in
the environment. The robot can then plan a route in the
environment, where the route can comprise one or more route poses.
The route poses can include a footprint indicative at least in part
of a pose, size, and shape of the robot along the route. Each route
pose can have a plurality of points therein. Based on forces
exerted on the points of each route pose by other route poses,
objects in the environment, and others, each route poses can
reposition. Based at least in part on interpolation performed on
the route poses (some of which may be repositioned), the robot can
dynamically route.
Inventors: |
Gabardos; Borja Ibarz;
(London, GB) ; Passot; Jean-Baptiste; (Solana
Beach, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Brain Corporation |
San Diego |
CA |
US |
|
|
Family ID: |
62021299 |
Appl. No.: |
16/454217 |
Filed: |
June 27, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16011499 |
Jun 18, 2018 |
10379539 |
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16454217 |
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15341612 |
Nov 2, 2016 |
10001780 |
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16011499 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G05D 2201/0203 20130101;
G01C 21/3415 20130101; A47L 11/4011 20130101; G05D 1/0214 20130101;
G05D 1/0088 20130101; G05D 1/0217 20130101; G05D 1/0276 20130101;
A47L 11/4061 20130101; G01C 21/343 20130101; G05D 1/0274 20130101;
A47L 2201/04 20130101 |
International
Class: |
G05D 1/02 20060101
G05D001/02; G01C 21/34 20060101 G01C021/34; A47L 11/40 20060101
A47L011/40; G05D 1/00 20060101 G05D001/00 |
Claims
1. A robot comprising: one or more sensors configured to collect
data about an environment including detected points on one or more
objects in the environment; and a controller configured to: create
a map of the environment based at least in part on the collected
data; determine a route in the map in which the robot will travel;
generate one or more route poses on the route, wherein each route
pose comprises a footprint indicative of poses of the robot along
the route and each route pose has a plurality of points disposed
therein; determine forces on each of the plurality of points of
each route pose, the forces comprising repulsive forces from one or
more of the detected points on the one or more objects and
attractive forces from one or more of the plurality of points on
others of the one or more route poses; reposition one or more route
poses in response to the forces on each point of the one or more
route poses; and perform interpolation between one or more route
poses to generate a collision-free path between the one or more
route poses for the robot to travel.
2. The robot of claim 1, wherein: the one or more route poses form
a sequence in which the robot travels along the route; and the
interpolation comprises a linear interpolation between sequential
ones of the one or more route poses.
3. The robot of claim 1, wherein the interpolation generates one or
more interpolation route poses having substantially similar
footprints to the footprint of each route pose.
4. The robot of claim 1, wherein the determination of the forces on
each point of the one or more route poses further comprises a
computation of a force function that associates, at least in part,
the forces on each point of each route pose with one or more
characteristics of objects in the environment.
5. The robot of claim 4, wherein the one or more characteristics
includes one or more of distance, shape, material, and color.
6. The robot of claim 4, wherein: the force function associates
zero repulsive force exerted by a first detected point on a first
object where a distance between the first detected point and a
second point of a first route pose is above a predetermined
distance threshold.
7. The robot of claim 1, wherein the footprint of each route pose
has substantially similar size and shape as the footprint of the
robot.
8. The robot of claim 1, wherein the robot comprises a floor
cleaner.
9. A method for dynamic navigation of a robot in an environment,
comprising: generating a map of the environment using data from one
or more sensors; determining a route on the map, the route
including one or more route poses, each route pose comprising a
footprint indicative at least in part of a pose and a shape of the
robot along the route and each route pose having a plurality of
points disposed therein; computing repulsive forces from a point on
an object in the environment onto the plurality of points of a
first route pose of the one or more route poses; repositioning the
first route pose in response to at least the repulsive forces; and
performing an interpolation between the repositioned first route
pose and another of the one or more route poses.
10. The method of claim 9, further comprising determining
attractive forces from a point on another of the one or more route
poses exerted on the plurality of points of the first route
pose.
11. The method of claim 9, further comprising: detecting a
plurality of objects in the environment with the one or more
sensors, each of the plurality of objects having detected points;
and defining a force function, the force function computing
repulsive forces exerted by each of the detected points of the
plurality of objects on the plurality of points of the first route
pose, wherein each repulsive force comprises a vector.
12. The method of claim 11, wherein repositioning the first route
pose comprises calculating a minimum of the force function.
13. The method of claim 9, wherein the repositioning of the first
route pose comprises translating and rotating the first route
pose.
14. The method of claim 9, wherein the interpolation comprises:
generating an interpolation route pose having a footprint
substantially similar to the shape of the robot; and determining a
translation and rotation of the interpolation route pose based at
least on a collision-free path between the translated and rotated
first route pose and the another of the one or more route
poses.
15. The method of claim 9, further comprising computing a magnitude
of the repulsive forces as proportional to a distance between the
point on the object and each of the plurality of points of the
first route pose if the point on the object is outside of the
footprint of the first route pose.
16. The method of claim 9, further comprising computing a magnitude
of the repulsive forces as inversely proportional to a distance
between the point on the object and each of the plurality of points
of the first route pose if the point on the object is inside the
footprint of the first route pose.
17. The method of claim 9, further comprising computing the torque
forces onto the plurality of points of the first route pose due to
the repulsive forces.
18. A non-transitory computer-readable storage apparatus having a
plurality of instructions stored thereon, the instructions being
executable by a processing apparatus to operate a robot, the
instructions configured to, when executed by the processing
apparatus, cause the processing apparatus to: generate a map of an
environment using data from one or more sensors; determine a route
on the map, the route comprising one or more route poses, each
route pose comprising a footprint indicative at least in part of a
pose and a shape of the robot along the route and each route pose
having a plurality of points disposed therein; and compute
repulsive forces from a point on an object in the environment onto
the plurality of points of a first route pose of the one or more
route poses.
19. The non-transitory computer-readable storage apparatus of claim
18, further comprising one or more instructions, which when
executed by the processing apparatus, further cause the processing
apparatus to determine attractive forces from a point on another of
the one or more route poses exerted on the plurality of points of
the first route pose.
20. The non-transitory computer-readable storage apparatus of claim
18, further comprising one or more instructions, which when
executed by the processing apparatus, further cause the processing
apparatus to determine torque forces from a point on another of the
one or more route poses exerted on the plurality of points of the
first route pose.
Description
PRIORITY
[0001] This application is a continuation of, and claims the
benefit of priority to, co-owned and co-pending U.S. patent
application Ser. No. 15/341,612 of the same title filed Nov. 2,
2016, the contents of which being incorporated herein by reference
in its entirety.
COPYRIGHT
[0002] A portion of the disclosure of this patent document contains
material that is subject to copyright protection. The copyright
owner has no objection to the facsimile reproduction by anyone of
the patent document or the patent disclosure, as it appears in the
Patent and Trademark Office patent files or records, but otherwise
reserves all copyright rights whatsoever.
BACKGROUND
Technological Field
[0003] The present application relates generally to robotics, and
more specifically to systems and methods for dynamic route planning
in autonomous navigation.
Background
[0004] Robotic navigation can be a complex problem. In some cases,
robots can determine a route to travel. By way of illustration, a
robot can learn routes demonstrated by a user (e.g., the user can
control a robot along a route and/or can upload a map containing a
route). As another illustration, a robot can plan its own route in
an environment based on its knowledge of the environment (e.g., a
map). However, a challenge that can occur is that after a robot
determines a route, features of the environment can change. For
example, items can fall into the path of the route and/or parts of
the environment can change. Current robots may not be able to make
real time adjustments to its planned path in response to these
changes (e.g., blockages). In such situations, current robots may
stop, collide into objects, and/or make sub-optimal adjustments to
its route. Accordingly, there is a need for improved systems and
methods for autonomous navigation, including systems and methods
for dynamic route planning.
SUMMARY
[0005] The foregoing needs are satisfied by the present disclosure,
which provides for, inter alia, apparatus and methods for dynamic
route planning in autonomous navigation. Example implementations
described herein have innovative features, no single one of which
is indispensable or solely responsible for their desirable
attributes. Without limiting the scope of the claims, some of the
advantageous features will now be summarized.
[0006] In a first aspect, a robot is disclosed. In one exemplary
implementation, the robot includes: one or more sensors configured
to collect data about an environment including detected points on
one or more objects in the environment; and a controller configured
to: create a map of the environment based at least in part on the
collected data, determine a route in the map in which the robot
will travel, generate one or more route poses on the route, wherein
each route pose comprises a footprint indicative of poses of the
robot along the route and each route pose has a plurality of points
disposed therein, determine forces on each of the plurality of
points of each route pose, the forces comprising repulsive forces
from one or more of the detected points on the one or more objects
and attractive forces from one or more of the plurality of points
on others of the one or more route poses, reposition one or more
route poses in response to the forces on each point of the one or
more route poses, and perform interpolation between one or more
route poses to generate a collision-free path between the one or
more route poses for the robot to travel.
[0007] In one variant, the one or more route poses form a sequence
in which the robot travels along the route; and the interpolation
comprises a linear interpolation between sequential ones of the one
or more route poses.
[0008] In another variant, the interpolation generates one or more
interpolation route poses having substantially similar footprints
to the footprint of each route pose. In another variant, the
determination of the forces on each point of the one or more route
poses further comprises computing a force function that associates,
at least in part, the forces on each point of each route pose with
one or more characteristics of objects in the environment.
[0009] In another variant, the one or more characteristics includes
one or more of distance, shape, material, and color. In another
variant, the force function associates zero repulsive force exerted
by a first detected point on a first object where a distance
between the first point and a second point of a first route pose is
above a predetermined distance threshold.
[0010] In another variant, the footprint of each route pose has
substantially similar size and shape as the footprint of the
robot.
[0011] In another variant, the robot comprises a floor cleaner.
[0012] In a second aspect, a method for dynamic navigation of a
robot is disclosed. In one exemplary implementation, the method
includes: generating a map of the environment using data from one
or more sensors; determining a route on the map, the route
including one or more route poses, each route pose comprising a
footprint indicative at least in part of a pose and a shape of the
robot along the route and each route pose having a plurality of
points disposed therein; computing repulsive forces from a point on
an object in the environment onto the plurality of points of a
first route pose of the one or more route poses; repositioning the
first route pose in response to at least the repulsive force; and
performing an interpolation between the repositioned first route
pose and another of the one or more route poses.
[0013] In one variant, determining attractive forces from a point
on another of the one or more route poses exerted on the plurality
of points of the first route pose. In another variant, detecting a
plurality of objects in the environment with the one or more
sensors, each of the plurality of objects having detected points;
and defining a force function, the force function computing
repulsive forces exerted by each of the detected points of the
plurality of objects on the plurality of points of the first route
pose, wherein each repulsive force is a vector.
[0014] In another variant, repositioning the first route pose
includes calculating the minimum of the force function.
[0015] In another variant, the repositioning of the first route
pose includes translating and rotating the first route pose.
[0016] In another variant, interpolation includes: generating an
interpolation route pose having a footprint substantially similar
to a shape of the robot; and determining a translation and rotation
of the interpolation route pose based at least on a collision-free
path between the translated and rotated first route pose and the
another of the one or more route poses.
[0017] In another variant, the method further comprising computing
a magnitude of the repulsive forces as proportional to a distance
between the point on the object and each of the plurality of points
of the first route pose if the point on the object is outside of
the footprint of the first route pose.
[0018] In another variant, computing a magnitude of the repulsive
forces as inversely proportional to a distance between the point on
the object and each of the plurality of points of the first route
pose if the point on the object is inside the footprint of the
first route pose.
[0019] In another variant, the method further includes computing
the torque forces onto the plurality of points of the first route
pose due to the repulsive forces.
[0020] In a third aspect, a non-transitory computer-readable
storage apparatus is disclosed. In one embodiment, the
non-transitory computer-readable storage apparatus has a plurality
of instructions stored thereon, the instructions being executable
by a processing apparatus to operate a robot. The instructions are
configured to, when executed by the processing apparatus, cause the
processing apparatus to: generate a map of the environment using
data from one or more sensors; determine a route on the map, the
route including one or more route poses, each route pose comprising
a footprint indicative at least in part of a pose and a shape of
the robot along the route and each route pose having a plurality of
points disposed therein; and compute repulsive forces from a point
on an object in the environment onto the plurality of points of a
first route pose of the one or more route poses.
[0021] In one variant, the instructions when executed by the
processing apparatus, further cause the processing apparatus to
determine attractive forces from a point on another of the one or
more route poses exerted on the plurality of points of the first
route pose.
[0022] In another variant, the instructions when executed by the
processing apparatus, further cause the processing apparatus to
determine torque forces from a point on another of the one or more
route poses exerted on the plurality of points of the first route
pose.
[0023] These and other objects, features, and characteristics of
the present disclosure, as well as the methods of operation and
functions of the related elements of structure and the combination
of parts and economies of manufacture, will become more apparent
upon consideration of the following description and the appended
claims with reference to the accompanying drawings, all of which
form a part of this specification, wherein like reference numerals
designate corresponding parts in the various figures. It is to be
expressly understood, however, that the drawings are for the
purpose of illustration and description only and are not intended
as a definition of the limits of the disclosure. As used in the
specification and in the claims, the singular form of "a", "an",
and "the" include plural referents unless the context clearly
dictates otherwise.
BRIEF DESCRIPTION OF THE DRAWINGS
[0024] The disclosed aspects will hereinafter be described in
conjunction with the appended drawings, provided to illustrate and
not to limit the disclosed aspects, wherein like designations
denote like elements.
[0025] FIG. 1 illustrates various side elevation views of exemplary
body forms for a robot in accordance with principles of the present
disclosure.
[0026] FIG. 2A is a diagram of an overhead view of a robot
navigating a path in accordance with some implementations of this
disclosure.
[0027] FIG. 2B illustrates an overhead view of a user demonstrating
a route to a robot before the robot autonomously travels a route in
an environment.
[0028] FIG. 3 is a functional block diagram of a robot in
accordance with some principles of this disclosure.
[0029] FIG. 4A is a top view diagram illustrating the interaction
between a robot and an obstacle in accordance with some
implementations of this disclosure.
[0030] FIG. 4B is a diagram of a global layer, intermediate layer,
and local layer in accordance with implementations of the present
disclosure.
[0031] FIG. 4C is a process flow diagram of an exemplary method for
dynamic route planning in accordance with some implementations of
this disclosure.
[0032] FIG. 4D illustrates an overhead view of route poses along
with repulsive forces exerted by objects in accordance with some
implementations of the present disclosure.
[0033] FIG. 4E illustrates example points on a route pose in
accordance with some implementations of the present disclosure.
[0034] FIG. 4F illustrates an overhead view showing attractive
forces between route poses in accordance with some implementations
of the present disclosure.
[0035] FIG. 5 is an overhead view of a diagram showing
interpolation between route poses in accordance with some
implementations of this disclosure.
[0036] FIG. 6 is a process flow diagram of an exemplary method for
operation of a robot in accordance with some implementations of
this disclosure.
[0037] FIG. 7 is a process flow diagram of an exemplary method for
operation of a robot in accordance with some implementations of
this disclosure.
[0038] All Figures disclosed herein are .COPYRGT. Copyright 2018
Brain Corporation. All rights reserved.
DETAILED DESCRIPTION
[0039] Various aspects of the novel systems, apparatuses, and
methods disclosed herein are described more fully hereinafter with
reference to the accompanying drawings. This disclosure can,
however, be embodied in many different forms and should not be
construed as limited to any specific structure or function
presented throughout this disclosure. Rather, these aspects are
provided so that this disclosure will be thorough and complete, and
will fully convey the scope of the disclosure to those skilled in
the art. Based on the teachings herein, one skilled in the art
should appreciate that the scope of the disclosure is intended to
cover any aspect of the novel systems, apparatuses, and methods
disclosed herein, whether implemented independently of, or combined
with, any other aspect of the disclosure. For example, an apparatus
can be implemented or a method can be practiced using any number of
the aspects set forth herein. In addition, the scope of the
disclosure is intended to cover such an apparatus or method that is
practiced using other structure, functionality, or structure and
functionality in addition to or other than the various aspects of
the disclosure set forth herein. It should be understood that any
aspect disclosed herein can be implemented by one or more elements
of a claim.
[0040] Although particular aspects are described herein, many
variations and permutations of these aspects fall within the scope
of the disclosure. Although some benefits and advantages of the
preferred aspects are mentioned, the scope of the disclosure is not
intended to be limited to particular benefits, uses, and/or
objectives. The detailed description and drawings are merely
illustrative of the disclosure rather than limiting, the scope of
the disclosure being defined by the appended claims and equivalents
thereof.
[0041] The present disclosure provides for improved systems and
methods for dynamic route planning in autonomous navigation. As
used herein, a robot can include mechanical or virtual entities
configured to carry out complex series of actions automatically. In
some cases, robots can be machines that are guided by computer
programs or electronic circuitry. In some cases, robots can include
electro-mechanical components that are configured for navigation,
where the robot can move from one location to another. Such
navigating robots can include autonomous cars, floor cleaners,
rovers, drones, carts, and the like.
[0042] As referred to herein, floor cleaners can include floor
cleaners that are manually controlled (e.g., driven or remote
control) and/or autonomous (e.g., using little to no user control).
For example, floor cleaners can include floor scrubbers that a
janitor, custodian, or other person operates and/or robotic floor
scrubbers that autonomously navigate and/or clean an environment.
Similarly, floor cleaners can also include vacuums, steamers,
buffers, mop, polishers, sweepers, burnishers, etc.
[0043] Detailed descriptions of the various implementations and
variants of the system and methods of the disclosure are now
provided. While many examples discussed herein are in the context
of robotic floor cleaners, it will be appreciated that the
described systems and methods contained herein can be used in other
robots. Myriad other example implementations or uses for the
technology described herein would be readily envisaged by those
having ordinary skill in the art, given the contents of the present
disclosure.
[0044] Advantageously, the systems and methods of this disclosure
at least: (i) provide for dynamic route planning in an autonomously
navigating robot; (ii) enhance efficiency in navigating
environments, which can allow for improved and/or efficient
utilization of resources (e.g., energy, fuel, cleaning fluid, etc.)
usage; and (iii) provide computational efficiency which can reduce
consumption of processing power, energy, time, and/or other
resources in navigating robots. Other advantages are readily
discernable by one having ordinary skill given the contents of the
present disclosure.
[0045] For example, many current robots that can autonomously
navigate are programmed to navigate a route and/or path to a goal.
In order to navigate these routes, these robots can create a path
plan (e.g., a global solution). Also, these robots can have
localized plans in a small area around it (e.g., in the order of a
few meters), where the robot can determine how it will navigate
around obstacles detected by its sensors (typically with basic
commands to turn when an object is detected). The robot can then
traverse the space in the pattern and avoid obstacles detected by
its sensors by, e.g., stopping, slowing down, deviating left or
right, etc. However, in many current applications, such traversal
and avoidance can be complicated and robots can either have
undesirable results (e.g., stoppages or collisions) and/or not be
able to navigate through more complex situations. In some cases,
such current applications can also be computationally expensive
and/or slow to run, causing robots to act unnaturally.
[0046] Advantageously, using systems and methods disclosed herein,
robots can deviate from its programming, following more efficient
paths and/or making more complex adjustments to avoid obstacles. In
some implementations described herein, such movements can be
determined in a more efficient, faster way, that also appears more
natural as a robot plans more complex paths.
[0047] A person having ordinary skill in the art would appreciate
that a robot, as referred to herein, can have a number of different
appearances/forms. FIG. 1 illustrates various side elevation views
of exemplary body forms for a robot in accordance with principles
of the present disclosure. These are non-limiting examples meant to
further illustrate the variety of body forms, but not to restrict
robots described herein to any particular body form. For example,
body form 100 illustrates an example where the robot is a stand-up
shop vacuum. Body form 102 illustrates an example where the robot
is a humanoid robot having an appearance substantially similar to a
human body. Body form 104 illustrates an example where the robot is
a drone having propellers. Body form 106 illustrates an example
where the robot has a vehicle shape having wheels and a passenger
cabin. Body form 108 illustrates an example where the robot is a
rover.
[0048] Body form 110 can be an example where the robot is a
motorized floor scrubber. Body form 112 can be a motorized floor
scrubber having a seat, pedals, and a steering wheel, where a user
can drive body form 112 like a vehicle as body form 112 cleans,
however, body form 112 can also operate autonomously. Other body
forms are further contemplated, including industrial machines that
can be robotized, such as forklifts, tugs, boats, planes, etc.
[0049] FIG. 2A is a diagram of an overhead view of robot 202
navigating a path 206 in accordance with some implementations of
this disclosure. Robot 202 can autonomously navigate through
environment 200, which can comprise various objects 208, 210, 212,
218. Robot 202 can start at an initial location and end at an end
location. As illustrated, the initial position and the end position
are substantially the same, illustrating a substantially closed
loop. However, in other cases, the initial location and the end
location may not be substantially the same, forming an open
loop.
[0050] By way of illustration, in some implementations, robot 202
can be a robotic floor cleaner, such as a robotic floor scrubber,
vacuum cleaner, steamer, mop, burnisher, sweeper, and the like.
Environment 200 can be a space having floors that are desired to be
cleaned. For example, environment 200 can be a store, warehouse,
office building, home, storage facility, etc. One or more of
objects 208, 210, 212, 218 can be shelves, displays, objects,
items, people, animals, or any other entity or thing that may be on
the floor or otherwise impede the robot's ability to navigate
through environment 200. Route 206 can be the cleaning path
traveled by robot 202 autonomously. Route 206 can follow a path
that weaves between objects 208, 210, 212, 218 as illustrated in
example route 206. For example, where objects 208, 210, 212, 218
are shelves in a store, robot 202 can go along the aisles of the
store and clean the floors of the aisles. However, other routes are
also contemplated, such as, without limitation, weaving back and
forth along open floor areas and/or any cleaning path a user could
use to clean the floor (e.g., if the user is manually operating a
floor cleaner). In some cases, robot 202 can go over a portion a
plurality of times. Accordingly, routes can overlap on themselves.
Accordingly, route 206 is meant merely as illustrative examples and
can appear differently as illustrated. Also, as illustrated, one
example of environment 200 is shown, however, it should be
appreciated that environment 200 can take on any number of forms
and arrangements (e.g., of any size, configuration, and layout of a
room or building) and is not limited by the example illustrations
of this disclosure.
[0051] In route 206, robot 202 can begin at the initial location,
which can be robot 202's starting point. Robot 202 can then clean
along route 206 autonomously (e.g., with little or no control from
a user) until it reaches an end location, where it can stop
cleaning. The end location can be designated by a user and/or
determined by robot 202. In some cases, the end location can be the
location in route 206 after which robot 202 has cleaned the desired
area of floor. As previously described, route 206 can be a closed
loop or an open loop. By way of illustrative example, an end
location can be a location for storage for robot 202, such as a
temporary parking spot, storage room/closet, and the like. In some
cases, the end location can be the point where a user training
and/or programming tasks for robot 202 stopped training and/or
programming.
[0052] In the context of floor cleaners (e.g., floor scrubbers,
vacuum cleaners, etc.), robot 202 may or may not clean at every
point along route 206. By way of illustration, where robot 202 is a
robotic floor scrubber, the cleaning system (e.g., water flow,
cleaning brushes, etc.) of robot 202 may only be operating in some
portions of route 206 and not others. For example, robot 202 may
associate certain actions (e.g., turning, turning on/off water,
spraying water, turning on/off vacuums, moving vacuum hose
positions, gesticulating an arm, raising/lowering a lift, moving a
sensor, turning on/off a sensor, etc.) with particular positions
and/or trajectories (e.g., while moving in a certain direction or
in a particular sequence along route 206) along the demonstrated
route. In the context of floor cleaners, such association may be
desirable when only some areas of the floor are to be cleaned but
not others and/or in some trajectories. In such cases, robot 202
can turn on a cleaning system in areas where a user demonstrated
for robot 202 to clean, and turn off the cleaning system
otherwise.
[0053] FIG. 2B illustrates an overhead view of a user demonstrating
route 216 to robot 202 before robot 202 autonomously travels route
206 in environment 200. In demonstrating route 216, a user can
start robot 202 at an initial location. Robot 202 can then weave
around objects 208, 210, 212, 218. Robot 202 can stop at an end
location, as previously described. In some cases (and as
illustrated), autonomously navigated route 206 can be exactly the
same as demonstrated route 216. In some cases, route 206 might not
be precisely the same as route 216, but can be substantially
similar. For example, as robot 202 navigates route 206, robot 202
uses its sensors to sense where it is in relationship to its
surrounding. Such sensing may be imprecise in some instances, which
may cause robot 202 to not navigate the precise route that had been
demonstrated and robot 202 had been trained to follow. In some
cases, small changes to environment 200, such as the moving of
shelves and/or changes in the items on the shelves, can cause robot
202 to deviate from route 216 when it autonomously navigates route
206. As another example, as previously described, robot 202 can
avoid objects by turning around them, slowing down, etc. when
autonomously navigating route 206. These objects might not have
been present (and avoided) when the user demonstrated route 216.
For example, the objects may be temporarily and/or transient items,
and/or may be transient and/or dynamic changes to the environment
200. As another example, the user may have done a poor job
demonstrating route 216. For example, the user may have crashed
and/or bumped into a wall, shelf, object, obstacle, etc. As another
example, an obstacle may have been present while the user had
demonstrated route 216, but no longer there when robot 202
autonomously navigates route 206. In these cases, robot 202 can
store in memory (e.g., memory 302) one or more actions that it can
correct, such as crashing and/or bumping to a wall, shelf, object,
obstacle, etc. When robot 202 then autonomously navigates
demonstrated route 216 (e.g., as route 206), robot 202 can correct
such actions and not perform them (e.g., not crash and/or bump into
a wall, shelf, object, obstacle, etc.) when it is autonomously
navigating. In this way, robot 202 can determine not to
autonomously navigate at least a portion of a navigable route, such
as a demonstrated route. In some implementations, determining not
to autonomously navigate at least a portion of the navigable route
includes determining when to avoid an obstacle and/or object.
[0054] As previously mentioned, as a user demonstrates route 216,
the user can turn on and off the cleaning system of robot 202, or
perform other actions, in order to train robot 202 where (e.g., at
what position), and/or along what trajectories, to clean along
route 216 (and subsequently when robot 202 autonomously cleans
route 206). The robot can record these actions in memory 302 and
later perform them when autonomously navigating. These actions can
include any actions that robot 202 may perform, such as turning,
turning on/off water, spraying water, turning on/off vacuums,
moving vacuum hose positions, gesticulating an arm,
raising/lowering a lift, moving a sensor, turning on/off a sensor,
etc.
[0055] FIG. 3 is a functional block diagram of a robot 202 in
accordance with some principles of this disclosure. As illustrated
in FIG. 3, robot 202 can include controller 304, memory 302, user
interfaces unit 308, exteroceptive sensors unit 306, proprioceptive
sensors unit 310, and communications unit 312, as well as other
components and subcomponents (e.g., some of which may not be
illustrated). Although a specific implementation is illustrated in
FIG. 3, it is appreciated that the architecture may be varied in
certain implementations as would be readily apparent to one of
ordinary skill given the contents of the present disclosure.
[0056] Controller 304 can control the various operations performed
by robot 202. Controller 304 can include one or more processors
(e.g., microprocessors) and other peripherals. As used herein,
processor, microprocessor, and/or digital processor can include any
type of digital processing device such as, without limitation,
digital signal processors ("DSPs"), reduced instruction set
computers ("RISC"), general-purpose ("CISC") processors,
microprocessors, gate arrays (e.g., field programmable gate arrays
("FPGAs")), programmable logic device ("PLDs"), reconfigurable
computer fabrics ("RCFs"), array processors, secure
microprocessors, specialized processors (e.g., neuromorphic
processors), and application-specific integrated circuits
("ASICs"). Such digital processors may be contained on a single
unitary integrated circuit die, or distributed across multiple
components.
[0057] Controller 304 can be operatively and/or communicatively
coupled to memory 302. Memory 302 can include any type of
integrated circuit or other storage device configured to store
digital data including, without limitation, read-only memory
("ROM"), random access memory ("RAM"), non-volatile random access
memory ("NVRAM"), programmable read-only memory ("PROM"),
electrically erasable programmable read-only memory ("EEPROM"),
dynamic random-access memory ("DRAM"), Mobile DRAM, synchronous
DRAM ("SDRAM"), double data rate SDRAM ("DDR/2 SDRAM"), extended
data output ("EDO") RAM, fast page mode RAM ("FPM"), reduced
latency DRAM ("RLDRAM"), static RAM ("SRAM"), "flash" memory (e.g.,
NAND/NOR), memristor memory, pseudostatic RAM ("PSRAM"), etc.
Memory 302 can provide instructions and data to controller 304. For
example, memory 302 can be a non-transitory, computer-readable
storage medium having a plurality of instructions stored thereon,
the instructions being executable by a processing apparatus (e.g.,
controller 304) to operate robot 202. In some cases, the
instructions can be configured to, when executed by the processing
apparatus, cause the processing apparatus to perform the various
methods, features, and/or functionality described in this
disclosure. Accordingly, controller 304 can perform logical and
arithmetic operations based on program instructions stored within
memory 302.
[0058] In some implementations, exteroceptive sensors unit 306 can
comprise systems and/or methods that can detect characteristics
within and/or around robot 202. Exteroceptive sensors unit 306 can
comprise a plurality and/or a combination of sensors. Exteroceptive
sensors unit 306 can include sensors that are internal to robot 202
or external, and/or have components that are partially internal
and/or partially external. In some cases, exteroceptive sensors
unit 306 can include exteroceptive sensors such as sonar, LIDAR,
radar, lasers, cameras (including video cameras, infrared cameras,
3D cameras, etc.), time of flight ("TOF") cameras, antenna,
microphones, and/or any other sensor known in the art. In some
implementations, exteroceptive sensors unit 306 can collect raw
measurements (e.g., currents, voltages, resistances gate logic,
etc.) and/or transformed measurements (e.g., distances, angles,
detected points in obstacles, etc.). Exteroceptive sensors unit 306
can generate data based at least in part on measurements. Such data
can be stored in data structures, such as matrices, arrays, etc. In
some implementations, the data structure of the sensor data can be
called an image.
[0059] In some implementations, proprioceptive sensors unit 310 can
include sensors that can measure internal characteristics of robot
202. For example, proprioceptive sensors unit 310 can measure
temperature, power levels, statuses, and/or any other
characteristic of robot 202. In some cases, proprioceptive sensors
unit 310 can be configured to determine the odometry of robot 202.
For example, proprioceptive sensors unit 310 can include
proprioceptive sensors unit 310, which can comprise sensors such as
accelerometers, inertial measurement units ("IMU"), odometers,
gyroscopes, speedometers, cameras (e.g. using visual odometry),
clock/timer, and the like. Odometry to facilitate autonomous
navigation of robot 202. This odometry can include robot 202's
position (e.g., where position includes robot's location,
displacement and/or orientation, and can sometimes be
interchangeable with the term pose as used herein) relative to the
initial location. In some implementations, proprioceptive sensors
unit 310 can collect raw measurements (e.g., currents, voltages,
resistances gate logic, etc.) and/or transformed measurements
(e.g., distances, angles, detected points in obstacles, etc.). Such
data can be stored in data structures, such as matrices, arrays,
etc. In some implementations, the data structure of the sensor data
can be called an image.
[0060] In some implementations, user interfaces unit 308 can be
configured to enable a user to interact with robot 202. For
example, user interfaces 308 can include touch panels, buttons,
keypads/keyboards, ports (e.g., universal serial bus ("USB"),
digital visual interface ("DVI"), Display Port, E-Sata, Firewire,
PS/2, Serial, VGA, SCSI, audioport, high-definition multimedia
interface ("HDMI"), personal computer memory card international
association ("PCMCIA") ports, memory card ports (e.g., secure
digital ("SD") and miniSD), and/or ports for computer-readable
medium), mice, rollerballs, consoles, vibrators, audio transducers,
and/or any interface for a user to input and/or receive data and/or
commands, whether coupled wirelessly or through wires. User
interfaces unit 308 can include a display, such as, without
limitation, liquid crystal display ("LCDs"), light-emitting diode
("LED") displays, LED LCD displays, in-plane-switching ("IPS")
displays, cathode ray tubes, plasma displays, high definition
("HD") panels, 4K displays, retina displays, organic LED displays,
touchscreens, surfaces, canvases, and/or any displays, televisions,
monitors, panels, and/or devices known in the art for visual
presentation. In some implementations user interfaces unit 308 can
be positioned on the body of robot 202. In some implementations,
user interfaces unit 308 can be positioned away from the body of
robot 202, but can be communicatively coupled to robot 202 (e.g.,
via communication units including transmitters, receivers, and/or
transceivers) directly or indirectly (e.g., through a network,
server, and/or a cloud).
[0061] In some implementations, communications unit 312 can include
one or more receivers, transmitters, and/or transceivers.
Communications unit 312 can be configured to send/receive a
transmission protocol, such as BLUETOOTH.RTM., ZIGBEE.RTM., Wi-Fi,
induction wireless data transmission, radio frequencies, radio
transmission, radio-frequency identification ("RFID"), near-field
communication ("NFC"), infrared, network interfaces, cellular
technologies such as 3G (3GPP/3GPP2), high-speed downlink packet
access ("HSDPA"), high-speed uplink packet access ("HSUPA"), time
division multiple access ("TDMA"), code division multiple access
("CDMA") (e.g., IS-95A, wideband code division multiple access
("WCDMA"), etc.), frequency hopping spread spectrum ("FHSS"),
direct sequence spread spectrum ("DSSS"), global system for mobile
communication ("GSM"), Personal Area Network ("PAN") (e.g.,
PAN/802.15), worldwide interoperability for microwave access
("WiMAX"), 802.20, long term evolution ("LTE") (e.g., LTE/LTE-A),
time division LTE ("TD-LTE"), global system for mobile
communication ("GSM"), narrowband/frequency-division multiple
access ("FDMA"), orthogonal frequency-division multiplexing
("OFDM"), analog cellular, cellular digital packet data ("CDPD"),
satellite systems, millimeter wave or microwave systems, acoustic,
infrared (e.g., infrared data association ("IrDA")), and/or any
other form of wireless data transmission.
[0062] As used herein, network interfaces can include any signal,
data, or software interface with a component, network, or process
including, without limitation, those of the FireWire (e.g., FW400,
FW800, FWS800T, FWS1600, FWS3200, etc.), universal serial bus
("USB") (e.g., USB 1.X, USB 2.0, USB 3.0, USB Type-C, etc.),
Ethernet (e.g., 10/100, 10/100/1000 (Gigabit Ethernet), 10-Gig-E,
etc.), multimedia over coax alliance technology ("MoCA"), Coaxsys
(e.g., TVNET.TM.), radio frequency tuner (e.g., in-band or OOB,
cable modem, etc.), Wi-Fi (802.11), WiMAX (e.g., WiMAX (802.16)),
PAN (e.g., PAN/802.15), cellular (e.g., 3G,
LTE/LTE-A/TD-LTE/TD-LTE, GSM, etc.), IrDA families, etc. As used
herein, Wi-Fi can include one or more of IEEE-Std. 802.11, variants
of IEEE-Std. 802.11, standards related to IEEE-Std. 802.11 (e.g.,
802.11 a/b/g/n/ac/ad/af/ah/ai/aj/aq/ax/ay), and/or other wireless
standards.
[0063] Communications unit 312 can also be configured to
send/receive a transmission protocol over wired connections, such
as any cable that has a signal line and ground. For example, such
cables can include Ethernet cables, coaxial cables, Universal
Serial Bus ("USB"), FireWire, and/or any connection known in the
art. Such protocols can be used by communications unit 312 to
communicate to external systems, such as computers, smart phones,
tablets, data capture systems, mobile telecommunications networks,
clouds, servers, or the like. Communications unit 312 can be
configured to send and receive signals comprising of numbers,
letters, alphanumeric characters, and/or symbols. In some cases,
signals can be encrypted, using algorithms such as 128-bit or
256-bit keys and/or other encryption algorithms complying with
standards such as the Advanced Encryption Standard ("AES"), RSA,
Data Encryption Standard ("DES"), Triple DES, and the like.
Communications unit 312 can be configured to send and receive
statuses, commands, and other data/information. For example,
communications unit 312 can communicate with a user operator to
allow the user to control robot 202. Communications unit 312 can
communicate with a server/network in order to allow robot 202 to
send data, statuses, commands, and other communications to the
server. The server can also be communicatively coupled to
computer(s) and/or device(s) that can be used to monitor and/or
control robot 202 remotely. Communications unit 312 can also
receive updates (e.g., firmware or data updates), data, statuses,
commands, and other communications from a server for robot 202.
[0064] In some implementations, one or the components and/or
subcomponents can be instantiated remotely from robot 202. For
example, mapping and localization units 262, may be located in a
cloud and/or connected to robot 202 through communications unit
312. Connections can be direct and/or through a server and/or
network. Accordingly, implementations of the functionality of this
disclosure should also be understood to include remote interactions
where data can be transferred using communications unit 312, and
one or more portions of processes can be completed remotely.
[0065] FIG. 4A is a top view diagram illustrating the interaction
between robot 202 and an obstacle 402 in accordance with some
implementations of this disclosure. In navigating route 216, robot
202 can encounter obstacle 402. Obstacle 402 can impede the path of
robot 202, which is illustrated as route portion 404. If robot were
to continue following on route portion 404, it may collide with
obstacle 402. However, in some circumstances, using exteroceptive
sensors unit 306 and/or proprioceptive sensors unit 310, robot 202
can stop before colliding with obstacle 402.
[0066] This interaction with obstacle 402 illustrates advantages of
implementations in accordance with the present disclosure. FIG. 4B
is a diagram of global layer 406, intermediate layer 408, and local
layer 410 in accordance with implementations of the present
disclosure. Global layer 406, intermediate layer 408, and local
layer 410 can be hardware and/or software layers instantiated in
one or more of memory 302 and/or controller 304. Global layer 406
can include software and/or hardware that implements global mapping
and routing. For example, the high-level mapping can include a map
of environment 200. The map can also include a representation of
route 216, allowing robot 202 to navigate the space in environment
200.
[0067] In some implementations, global layer 406 can include a
global planner. In this way, global layer 406 can determine one or
more of: the location of robot 202 (e.g., in global coordinates
such as two-dimensional coordinates, three-dimensional coordinates,
four-dimensional coordinates, etc.); the path robot 202 should take
to reach its goal; and/or higher-level (e.g., long-range) planning.
In this way, robot 202 can determine its general path and/or
direction to travel from one location to another.
[0068] Local layer 410 includes software and/or hardware that
implements local planning. For example, local layer 410 can include
short-range planning configured for maneuvering in local
constraints of motion. Local layer 410 can process data received
from exteroceptive sensors unit 306 and determine the presence
and/or positioning of obstacles and/or objects near robot 202. For
example, if an object is within range of a sensor of exteroceptive
sensors unit 306 (e.g., a LIDAR, sonar, camera, etc.), robot 202
can detect the object. The local layer 410 can compute and/or
control motor functionality to navigate around objects, such by
controlling actuators to turn, move forward, reverse, etc. In some
cases, processing in local layer 410 can be computationally
intensive. For example, local layer 410 can receive data from
sensors of exteroceptive sensors unit 306 and/or proprioceptive
sensors unit 310. Local layer 410 can then determine motor
functions to avoid an object detected by exteroceptive sensors unit
306 (e.g., using a motor to turn a steering column left and right,
and/or using a motor to push the robot forward). The interplay of
local layer 410 and global layer 406 can allow robot 202 to make
local adjustments while still moving generally along a route to its
goal.
[0069] However, in some circumstances, it can be desirable to make
adjustments at a finer level than what would be computed by global
layer 406, yet not at the computationally intensive level of
precise motor functions of local layer 410. Accordingly,
intermediate layer 408 can include hardware and/or software that
can determine intermediate adjustments of robot 202 as it navigates
around objects.
[0070] In intermediate layer 408, robot 202 can plan how to avoid
objects and/or obstacles in its environment. In some cases,
intermediate layer 408 can be initialized with at least a partial
path and/or route from a global path planner from global layer
406.
[0071] Because objects (e.g., obstacles, walls, etc.) present
things in which robot 202 could collide, objects and/or obstacles
can put forth a repulsive force on robot 202. In some cases, by
objects repulsing robot 202, robot 202 can navigate along a
collision-free path around those objects and/or obstacles.
[0072] FIG. 4C is a process flow diagram of an exemplary method 450
for dynamic route planning in accordance with some implementations
of this disclosure. In some implementations, method 450 can be
performed by intermediate layer 408 and/or by controller 304. Block
452 can include obtaining a route containing one or more route
poses. In some cases, this route can be created by robot 202 and/or
uploaded onto robot 202. In some cases, the route can be passed
from global layer 406 to intermediate layer 408. Block 454 can
include selecting a first route pose. Block 456 can include, for
the first route pose, determining repulsive forces from objects in
the environment. Block 458 can include, for the first route pose,
determining attractive forces from other route poses. Block 460 can
include determining the translation and/or rotation of the first
route pose due to the repulsive forces and attractive forces. Block
462 can include performing interpolation to account for the
translated and/or rotated route pose. This process and others will
be illustrated throughout this disclosure.
[0073] By way of illustration, FIG. 4D illustrates route poses 414
and 416 along with repulsive forces exerted by objects in
accordance with some implementations of the present disclosure. For
example, the points on a route can be discretized locations along
the path, such as route poses, illustrating the pose of robot 202
throughout its route. In some cases, such discretized locations can
also have associated probabilities, such as particles or bubbles.
Route poses can identify the position and/or orientation that robot
202 would travel on the route. In a planar application, the route
pose can include (x, y, .theta.) coordinates. In some cases,
.theta. can be the heading of the robot in the plane. The route
poses can be regularly or irregularly spaced on robot 202's route.
In some cases, intermediate layer can obtain the route containing
one or more route poses from global layer 406, as described in
block 452 of method 450. In some implementations, route poses can
form a sequence, wherein robot 202 travels between sequential route
poses on a route. For example, route poses 414 and 416 could be a
sequence of route poses where robot 202 travels to route pose 414
and then to route pose 416.
[0074] By way of illustrative example, route poses 414 and 416
illustrate discretized locations along the route portion 404. This
illustrative example shows route poses 414 and 416 as shaped as
robot 202, with substantially similar footprints. The footprints of
route poses 414 and 416 can be adjusted in size depending on how
conservative one desires to be with respect to robot collisions. A
smaller footprint can present higher likelihoods of a collision,
but such a smaller footprint can allow robot 202 to clear more
areas that it should be able to as it autonomously navigates. A
larger footprint might decrease the likelihood of a collision, but
robot 202 would not go through some places autonomously that it
otherwise should be able to. The footprint can be predetermined by
a footprint parameter that sets the size (e.g., scales) of the
footprint of robot 202, as illustrated in route poses (e.g., route
poses 414 and 416). In some cases, there can be a plurality of
footprint parameters that control the sizes of route poses of robot
202 asymmetrically.
[0075] In FIG. 4D, while route poses 414 and 416 are illustrated
and described, it should be appreciated by someone having ordinary
skill in the art that there can be any number of route poses
throughout a route, and the descriptions of the implementations of
this disclosure can be applied to those route poses.
Advantageously, having route poses 414 and 416 shaped like robot
202 (e.g., a footprint of robot 202) can allow robot 202 to
determine places in which robot 202 can fit while travelling. The
footprint parameter(s) can be used to adjust how robot 202 projects
itself. For example, a larger footprint used in route poses 414
and/or 416 can be more conservative in that it can cause, at least
in part, robot 202 to travel further away from objects. In
contrast, a smaller footprint can cause, at least in part, robot
202 to travel closer to objects. Route poses (e.g., route poses 414
and 416) can be of different sizes from one another. By way of
illustration, it may be desirable for robot 202 to be more
conservative in certain scenarios, such as on turns. Accordingly,
in this illustration, the footprint of route poses on turns can be
larger than the footprint of route poses on straightaways. Such
dynamic reshaping of route poses can be performed by making the
size of the route poses dependent on the rotation of the route pose
relative to other route poses, or the changes in translation and/or
rotation of route pose. One or more of the route poses on a route
(e.g., route poses 414 and/or 416) can also be a different shape
other than the shape of robot 202. For example, the route poses can
be circular, square, triangular, and/or any other shape.
[0076] As described in block 454 from method 450, one can observe
either route poses 414 or 416 as a first route pose. However, for
purposes of illustration, and to illustrate the breadth of the
described implementations of this disclosure, route poses 414 and
416 will be described together.
[0077] Points along objects (e.g., points determined by mapping,
detecting by sensors of exteroceptive sensors unit 306, etc.) can
exert a repulsive force on route poses of robot 202 (e.g., route
poses 414 and 416). In this way, the objects can, conceptually,
prevent robot 202 from colliding into them. In some cases, these
points can represent at least in part poses and/or sets of poses.
For example, arrows 412 illustrate repulsive forces from points
along object 210.
[0078] In some implementations, the forces exerted by points by
objects may be uniform in that each point on route poses 414 and
416 can have substantially similar forces exerted on them. However,
in other implementations, the forces exerted by points of objects
on route poses 414 and 416 may not be uniform and may vary based on
a force function.
[0079] By way of illustration, a force function (e.g., a repulsive
force function) can in some cases determine at least in part the
repulsive force exerted on a point on route poses 414 or 416 by an
object. For example, the force functions can be used in block 456
of method 450 to determine the repulsive forces from objects in the
environment for a first route pose (e.g., a first route pose of
route poses 414 and 416). In some implementations, the force
function can be dependent on characteristics of where an object
appears relative to route poses 414 and 416. The force function can
then represent the force experienced by points route poses 414 and
416 (e.g., one or more points on the surface of route poses 414 and
416, the center of route poses 414 and 416, the center of mass of
route poses 414 and 416, and/or any point of and/or around route
poses 414 and 416). Because the forces can be dependent on their
direction and magnitudes, repulsive forces (and/or attractive
forces) can be vectors. In some cases, repulsive forces can exert
rotational forces on a route pose, which can manifest in torque
forces.
[0080] For example, repulsion forces and torque forces can be
calculated at n different poses along a path. In some cases, these
n different poses can be associated with route poses. Each pose can
consist of m points in a footprint. In some cases, these m points
can be points on the route poses.
[0081] In some cases, a plurality of points can define the body of
robot 202 as reflected in route poses 414 and 416, providing
representative coverage over a portion of the body of robot 202
and/or substantially all of robot 202. For example, 15-20 points
can be distributed throughout the surface and/or interior of robot
202 and be reflected in route poses 414 and 416. However, in some
cases, there can be fewer points. FIG. 4E illustrates example
points on route pose 414, such as point 418. Each point can
experience, at least in part, the forces (e.g., repulsive forces)
placed on it by objects in the surrounding of route poses 414.
[0082] Advantageously, by having a plurality of points on the body
of route poses 414 and 416 that can experience forces, points of
route poses 414 and 416 can translate and/or rotate relative to one
another, causing, at least in part, repositioning (e.g.,
translation and/or rotation) of route poses 414 and 416. These
translations and/or rotations of route poses 414 and 416 can cause
deformations of the route navigated by robot 202.
[0083] Torsion forces can occur when different points on a route
pose experience different magnitudes and directions of forces.
Accordingly, the torsion force can cause the route poses to rotate.
In some cases, predetermined parameters can define at least in part
the torsion experienced by route poses 414 and 416. For example, a
predetermined torsion parameter can include a multiplier for the
rotational forces experience on a point on route poses 414 or 416.
This predetermined torsion parameter can be indicative of force due
to misalignment of route poses 414 or 416 and the path. In some
cases, the predetermined torsion parameter may vary based on
whether the force is repulsive or cohesive.
[0084] Returning to FIG. 4D, a characteristic on which the force
function depends in part can be a position of a point on an object
relative to route poses 414 and 416. Distance can be determined
based at least in part on sensors of exteroceptive sensors unit
306. As a first example, the repulsive force exerted onto route
poses 414 and 416 from a point on an object exterior to robot 202
(e.g., not within the footprint of route poses 414 and 416 such as
points on obstacles 210 and 212 as illustrated) can be
characterized at least in part by the function r(d).varies.1/d,
where r is the repulsion of a point on an object and d is the
distance between the point on an object and a point on route pose
414 or route pose 416. In this way, the repulsion of a point on an
object is inversely proportional to the distance between the point
on the object and the point on route pose 414 or route pose 416.
Advantageously, such a function allows objects close to route poses
414 and 416 to exert more repulsion, and thereby potentially more
strongly influence the course of robot 202 to avoid a collision
than objects further away.
[0085] In some cases, a predetermined repulsive distance threshold
can be put on the distance between a point on route pose 414 and
route pose 416 and a point on an object. This predetermined
repulsive distance threshold can be indicative at least in part of
the maximum distance between a points on either route pose 414 and
route pose 416 and a point on an object in which the point on the
object can exert a repulsive force (and/or a torsion force) on
points on either route poses 414 and 416. Accordingly, when a point
on an object is a distance (e.g., from a point on either route pose
414 and route pose 416) that is above (or equal to and/or above,
depending on the definition of the threshold), the repulsive force
and/or torsion force can be zero or substantially zero.
Advantageously, having a predetermined repulsive distance threshold
can, in some cases, prevent some points on objects from exerting
forces on points on route poses 414 and 416. In this way, when
there is a predetermined repulsive distance, robot 202 can get
closer to certain objects and/or not be influenced by further away
objects.
[0086] As a second example, the repulsive force exerted onto route
poses 414 and 416 from a point on the interior of route poses 414
and 416 (e.g., within the footprint of route poses 414 and 416).
For example, object 402 has portion 420 that appears interior to
route pose 416. In these cases, a different force function can be
exerted by points of object 402 in portion 420 onto points of route
pose 416 in portion 420. In some implementations, this force can be
characterized at least in part by the function r(d).varies.d, where
the variables are as described above. Advantageously, by having a
different force function defined for interior objects, route pose
416 can move asymmetrically causing rotations.
[0087] In some implementations, the force function can also depend
on other characteristics of objects, such as shape, material,
color, and/or any other characteristic of the object. These
characteristics can be determined by one or more of sensors of
exteroceptive sensors 306 in accordance with known methods in the
art. Advantageously, taking into account characteristics can be
further informative of how robot 202 should navigate around
objects. In some instances, the cost map can be used to compute
additional repulsion values based on these characteristics.
[0088] For example, the shape of an object can be indicative at
least in part of an associated repercussion of collision. By way of
illustration, a humanoid shape may be indicative of a person. As
such, an object detected with this shape can place a greater
repulsive force on route poses 414 and 416 in order to push the
path further away from the humanoid shape. As another example, the
shape of an object can be indicative in part of increased damage
(e.g., to the object or robot 202) if a collision occurred. By way
of illustration, pointed objects, skinny objects, irregular
objects, predetermined shapes (e.g., vase, lamp, display, etc.)
and/or any other shape can be indicative at least in part of
resulting in increased damage. Size may be another characteristic
of shape that can be taken into account. For example, smaller
objects may be more fragile in the event of a collision, but larger
objects could cause more damage to robot 202. In the case of size,
force functions can take into account the size of the objects so
that the points on those objects repulse points on route poses 414
and 416 proportionally as desired. By way of illustration, if route
pose 414 is between a larger object and a smaller object, if points
of the larger object have a relatively larger repulsive force as
defined at least in part on the force function, route pose 414 will
be pushed relatively closer to the smaller object. If the points of
the smaller object have a relatively larger repulsive force as
defined at least in part on the force function, route pose 414 will
be pushed relatively closer to the larger object. Accordingly, the
repulsive force on route poses 414 and 416 can be adjusted based at
least in part on the shape. The shape can be detected at least in
part by sensors of exteroceptive sensors unit 306. As another
illustrative example, walls can be identified in a cost map, and a
repulsive force can be associated with walls due to their size and
shape.
[0089] In some implementations, the force function can also depend
on the material of the objects. For example, certain materials can
be indicative at least in part of more damage if a collision
occurred. By way of illustration, glass, porcelain, mirrors, and/or
other fragile material can prove to be more damaging in the event
of a collision. In some cases, such as in the case of mirrors, the
material can sometimes cause errors in the sensors of exteroceptive
sensor units 306. Accordingly, in some cases, it may be desirable
for robot 202 to navigate further away from such objects, which can
be reflected in the force function (e.g., increasing the repulsion
force exerted by points on objects of some materials versus other
materials).
[0090] In some implementations, color can be detected by sensors of
exteroceptive sensor units 306. The force function can be dependent
at least in part on the color of an object and/or points on an
object. For example, certain objects in an environment may be a
certain color (e.g., red, yellow, etc.) to indicate at least in
part that robot 202 (or in some cases people) should be cautious of
those objects. Accordingly, in some cases, it may be desirable for
robot 202 to navigate further away from such objects, which can be
reflected in the force function.
[0091] In some implementations, the force function can be dependent
on other factors, such as the location of an object. For example,
certain areas of a map (e.g., as passed from global layer 406) can
have characteristics. By way of illustration, some areas of the map
(e.g., a cost map) can be areas in which robot 202 should not pass.
There can also can be places where robot 202 cannot go into because
they are not accessible (such as into an object). Accordingly, in
some cases, the force function can be adjusted to account for such
places. In some implementations, the force function can cause
points in those places to exert no force (or substantially no
force) on points on route poses 414 and 416. Advantageously, no
force can be reflective of regions where robot 202 would not go
(e.g., inside objects and the like). In contrast, in some
implementations, such places can be treated as obstacles, exerting
a repulsive force on route poses 414 and 416. Advantageously,
having such a repulsion force can keep robot 202 from attempting to
enter such areas.
[0092] In some implementations, not all forces on route poses 414
and 416 are repulsive. For example, points on route poses (e.g.,
route poses 414 and 416) can exert attractive (e.g., cohesive)
forces, which can, at least in part, pull route poses towards each
other. FIG. 4F illustrates attractive forces between route poses
414 and 416 in accordance with some implementations of the present
disclosure. The arrows are indicative at least in part that route
poses are drawn towards each other along route portion 404.
Advantageously, the cohesive force between route poses can cause,
at least in part, robot 202 towards following a path substantially
similar to the path planned by global layer 406 (e.g., a route
substantially similar to an original route, such as an originally
demonstrated route that robot 202 should follow in the absence of
objects around which to navigate).
[0093] The cohesive force can be set by a force function (e.g., a
cohesive force function), which can be dependent on characteristics
of the path, such as the spacing distance between route
poses/particles, the smoothness of the path, how desirable it is
for robot 202 to follow a path, etc. In some cases, the cohesive
force function can be based at least in part on a predetermined
cohesion multiplier, which can determine at least in part the force
pulling route poses together. A lower predetermined cohesion
multiplier can reduce the cohesive strength of route portion 404
(e.g., draw of route poses towards it) and, in some cases, may
cause a loss in smoothness of the path travelled by robot 202. In
some cases, only sequential route poses exert cohesive forces on
the points of one another. In other cases, all route poses exert
cohesive forces on one another. In still other cases, some route
poses exert cohesive forces on others. The determination of which
route poses are configured to exert cohesive forces on one another
can depend on a number of factors, which may vary on a case-by-case
basis. For example, if a route is circular, it may be desirable for
all route poses to exert cohesive forces on one another to tighten
the circle. As another example, if the route is complex, then it
may be desirable for certain complex paths to only have sequential
route poses exert cohesive forces on one another. This limitation
may allow robot 202 to make more turns and/or have more predictable
results because other positioned route poses will not unduly
influence it. Ones between the aforementioned examples in
complexity may have some of the route poses exerting cohesive
forces. As another example, the number of route poses may also be a
factor. Having a lot of route poses on a route may cause unexpected
results if all of them exert cohesive forces on one another. If
there are fewer route poses, this might not be a problem, and all
or some of the route poses can exert forces. In some cases, there
can be a predetermined cohesive force distance threshold, where if
a point on a first route pose is distance that is more than the
predetermined cohesive force distance threshold (or more than or
equal to, depending on how it is defined) from a point on a second
route pose, the cohesive force can be zero or substantially
zero.
[0094] In some implementations the cohesive force function and the
repulsive force function can be the same force function. In other
implementations, the cohesive force function and the repulsive
force functions are separate. The cohesive force function can be
used to determine the attractive forces from other route poses in
accordance with block 458 from method 450. In some implementations,
both the cohesive forces and repulsive forces can result in torsion
(e.g., causing rotation) of a route pose.
[0095] As described with reference to intermediate layer 408, route
poses 414 and 416 can experience different attractive and repulsive
forces. In some implementations, the forces can be stored in
arrays. For example, there can be an array of forces indicative of
repulsion, torsion, cohesion, etc.
[0096] In some cases, forces can be toggled, such as by using an
on/off parameter that can turn on or off any individual force
and/or group of forces from a point. For example, the on/off
parameter can be binary wherein one value turns the force on and
another turns the force off. In this way, some forces can be turned
off, such as based on the distance an object is from a route pose,
whether a point is in the interior of an object or no go zone,
distance between route poses, etc.
[0097] On the balance, the net forces on route poses 414 and 416
can reposition one or more of route poses 414 and 416. For example,
route poses 414 and 416 can be displaced. Route poses 414 and 416
can displace (e.g., translated and/or rotated) until their net
forces, in any direction, are substantially zero and/or minimized.
In this way, route poses 414 and 416 can be displaced to locations
indicative at least in part to an adjusted route for robot 202 to
travel to avoid objects (e.g., obstacle 402). The translation
and/or rotation of a route pose due to the repulsive forces and
attractive forces can be determined in accordance with block 460 of
method 450.
[0098] There can be different adjustments made to determining the
displacement of route poses 414 and 416. For example, in some
cases, instead of considering all forces on route poses 414 and
416, attractive forces may only be considered. Advantageously, such
a system can allow robot 202 to stick to static paths. Based at
least in part on the displacement of route poses 414 and 416, robot
202 can set a new path for the route planner. In the new path, the
trajectory can be representative of a point on robot 202, such as
the center of robot 202, as robot 202 travels the path.
[0099] After robot 202 determines the displacement of route poses
414 and 416, robot 202 can determine a path to travel. For example,
based on the positions (e.g., locations and/or orientations) of
route poses 414 and 416, robot 202 can determine the path to
navigate to and/or between route poses 414 and 416, and/or any
other route poses from its present location. In some cases, robot
202 will travel between consecutive (e.g., sequential) route poses
in order, defining at least in part a path. For example, this
determination can be based at least in part on an interpolation
between route poses taking into account the path robot 202 can
travel between those points. In many cases, linear interpolation
can be used. By using performing interpolation, robot 202 can
account for the translated and/or rotated route pose in accordance
with block 462 in method 450.
[0100] FIG. 5 is an overhead view of a diagram showing
interpolation between route poses 414 and 416 in accordance with
some implementations of this disclosure. Based on forces placed on
route poses 414 and 416, as described herein, route poses 414 and
416 have displaced. As illustrated, route pose 414 has both
translated and rotated. The translation can be measured in standard
units, such as inches, feet, meters, or any other unit of
measurement (e.g., measurements in the metric, US, or other system
of measurement) and/or relative/non-absolute units, such as ticks,
pixels, percentage of range of a sensor, and the like. Rotation can
be measured in degrees, radians, etc. Similarly, route pose 416 has
also been translated and/or rotated. Notably, both route poses 414
and 416 clear obstacle 402. Since route poses 414 and 416 represent
discretized locations along a path travelled by robot 202, robot
202 can interpolate between them to determine the path it should
take. Interpolated poses 502A-502D illustrate a path travelled
between route poses 414 and 416. Notably, robot 202 may also
interpolate other paths (not illustrated) to move to route poses
and/or between route poses.
[0101] Interpolated poses 502A-502D can have associated footprints
substantially similar to the footprints of one or more of route
poses 414 and 416. In some cases, as illustrated in FIG. 5,
interpolated poses 502A-502D can be interpolated route poses.
Accordingly, interpolated poses 502A-502D can represent the
position and/or orientation that robot 202 would be along a route.
Advantageously, this can allow the interpolated path to guide robot
202 to places where robot 202 would fit. Moreover, interpolated
poses 502A-502D can be determined such that there is no overlap
between the footprint of any one of interpolated poses 502-502D and
an object (e.g., obstacle 402, object 210, or object 212), thereby
avoiding collisions.
[0102] Interpolated poses 502A-502D can also be determined taking
into account the rotation and/or translation to get from route pose
414 to route pose 416. For example, robot 202 can determine the
pose of route pose 414 and the pose of route pose 416. Robot 202
can then find the difference between the poses of route poses 414
and route poses 416, and then determine how to get from the pose of
route pose 414 to the pose of route pose 416. For example, robot
202 can distribute the rotation and translation between
interpolated poses 502A-502D such that robot 202 would rotate and
translate from route pose 414 to route pose 416. In some cases,
robot 202 can distribute the rotation and translation substantially
equally between interpolated poses 502A-502D. For example, if there
are N number of interpolation positions, robot 202 can divide the
difference in location and rotation of the poses of route poses 414
and 416 substantially evenly across those N number of interpolation
positions. Alternatively, robot 202 can divide the difference in
location and/or rotation of the poses of route poses 414 and 416
un-evenly across those N number of interpolation positions.
Advantageously, even division can allow for robot 202 to travel
smoothly from route pose 414 to route pose 416. However, un-even
division can allow robot 202 to more easily account for and avoid
objects by allowing finer movements in some areas as compared to
others. For example, in order to avoid an object in which
interpolated poses 502A-502D comes near, robot 202 would have to
make a sharp turn. Accordingly, more interpolated poses around that
turn may be desirable in order to account for the turn. In some
cases, the number of interpolation positions can be dynamic, and
more or fewer than N number of interpolation positions can be used
as desired.
[0103] FIG. 6 is a process flow diagram of an exemplary method 600
for operation of a robot in accordance with some implementations of
this disclosure. Block 602 includes creating a map of the
environment based at least in part on collected data. Block 604
includes determining a route in the map in which the robot will
travel. Block 606 includes generating one or more route poses on
the route, wherein each route pose comprises a footprint indicative
of poses of the robot along the route and each route pose has a
plurality of points therein. Block 608 includes determining forces
on each of the plurality of points of each route pose, the forces
comprising repulsive forces from one or more of the detected points
on the one or more objects and attractive forces from one or more
of the plurality of points on others of the one or more route
poses. Block 610 includes repositioning each route pose in response
to the forces on each point of each route pose. Block 612 includes
perform interpolation between the one or more repositioned route
poses to generate a collision-free path between the one or more
route poses for the robot to travel.
[0104] FIG. 7 is a process flow diagram of an exemplary method 700
for operation of a robot in accordance with some implementations of
this disclosure. Block 702 includes generating a map of the
environment using data from one or more sensors. Block 704 includes
determining a route on the map, the route including one or more
route poses, each route pose comprising a footprint indicative at
least in part of a pose, size, and shape of the robot along the
route and each route pose having a plurality of points therein.
Block 706 includes computing repulsive forces from a point on an
object in the environment onto the plurality of points of a first
route pose of the one or more route poses. Block 708 includes
repositioning the first route pose in response to at least the
repulsive force. Block 710 includes performing an interpolation
between the repositioned first route pose and another of the one or
more route poses.
[0105] As used herein, computer and/or computing device can
include, but are not limited to, personal computers ("PCs") and
minicomputers, whether desktop, laptop, or otherwise, mainframe
computers, workstations, servers, personal digital assistants
("PDAs"), handheld computers, embedded computers, programmable
logic devices, personal communicators, tablet computers, mobile
devices, portable navigation aids, J2ME equipped devices, cellular
telephones, smart phones, personal integrated communication or
entertainment devices, and/or any other device capable of executing
a set of instructions and processing an incoming data signal.
[0106] As used herein, computer program and/or software can include
any sequence or human or machine cognizable steps which perform a
function. Such computer program and/or software may be rendered in
any programming language or environment including, for example,
C/C++, C#, Fortran, COBOL, MATLAB.TM., PASCAL, Python, assembly
language, markup languages (e.g., HTML, SGML, XML, VoXML), and the
like, as well as object-oriented environments such as the Common
Object Request Broker Architecture ("CORBA"), JAVA.TM. (including
J2ME, Java Beans, etc.), Binary Runtime Environment (e.g., BREW),
and the like.
[0107] As used herein, connection, link, transmission channel,
delay line, and/or wireless can include a causal link between any
two or more entities (whether physical or logical/virtual), which
enables information exchange between the entities.
[0108] It will be recognized that while certain aspects of the
disclosure are described in terms of a specific sequence of steps
of a method, these descriptions are only illustrative of the
broader methods of the disclosure, and may be modified as required
by the particular application. Certain steps may be rendered
unnecessary or optional under certain circumstances. Additionally,
certain steps or functionality may be added to the disclosed
implementations, or the order of performance of two or more steps
permuted. All such variations are considered to be encompassed
within the disclosure disclosed and claimed herein.
[0109] While the above detailed description has shown, described,
and pointed out novel features of the disclosure as applied to
various implementations, it will be understood that various
omissions, substitutions, and changes in the form and details of
the device or process illustrated may be made by those skilled in
the art without departing from the disclosure. The foregoing
description is of the best mode presently contemplated of carrying
out the disclosure. This description is in no way meant to be
limiting, but rather should be taken as illustrative of the general
principles of the disclosure. The scope of the disclosure should be
determined with reference to the claims.
[0110] While the disclosure has been illustrated and described in
detail in the drawings and foregoing description, such illustration
and description are to be considered illustrative or exemplary and
not restrictive. The disclosure is not limited to the disclosed
embodiments. Variations to the disclosed embodiments can be
understood and effected by those skilled in the art in practicing
the claimed disclosure, from a study of the drawings, the
disclosure and the appended claims.
[0111] It should be noted that the use of particular terminology
when describing certain features or aspects of the disclosure
should not be taken to imply that the terminology is being
re-defined herein to be restricted to include any specific
characteristics of the features or aspects of the disclosure with
which that terminology is associated. Terms and phrases used in
this application, and variations thereof, especially in the
appended claims, unless otherwise expressly stated, should be
construed as open ended as opposed to limiting. As examples of the
foregoing, the term "including" should be read to mean "including,
without limitation," "including but not limited to," or the like;
the term "comprising" as used herein is synonymous with
"including," "containing," or "characterized by," and is inclusive
or open-ended and does not exclude additional, unrecited elements
or method steps; the term "having" should be interpreted as "having
at least;" the term "such as" should be interpreted as "such as,
without limitation;" the term `includes" should be interpreted as
"includes but is not limited to;" the term "example" is used to
provide exemplary instances of the item in discussion, not an
exhaustive or limiting list thereof, and should be interpreted as
"example, but without limitation;" adjectives such as "known,"
"normal," "standard," and terms of similar meaning should not be
construed as limiting the item described to a given time period or
to an item available as of a given time, but instead should be read
to encompass known, normal, or standard technologies that may be
available or known now or at any time in the future; and use of
terms like "preferably," "preferred," "desired," or "desirable,"
and words of similar meaning should not be understood as implying
that certain features are critical, essential, or even important to
the structure or function of the present disclosure, but instead as
merely intended to highlight alternative or additional features
that may or may not be utilized in a particular embodiment.
Likewise, a group of items linked with the conjunction "and" should
not be read as requiring that each and every one of those items be
present in the grouping, but rather should be read as "and/or"
unless expressly stated otherwise. Similarly, a group of items
linked with the conjunction "or" should not be read as requiring
mutual exclusivity among that group, but rather should be read as
"and/or" unless expressly stated otherwise. The terms "about" or
"approximate" and the like are synonymous and are used to indicate
that the value modified by the term has an understood range
associated with it, where the range can be .+-.20%, .+-.15%,
.+-.10%, .+-.5%, or .+-.1%. The term "substantially" is used to
indicate that a result (e.g., measurement value) is close to a
targeted value, where close can mean, for example, the result is
within 80% of the value, within 90% of the value, within 95% of the
value, or within 99% of the value. Also, as used herein "defined"
or "determined" can include "predefined" or "predetermined" and/or
otherwise determined values, conditions, thresholds, measurements,
and the like.
* * * * *