U.S. patent application number 09/994447 was filed with the patent office on 2007-03-15 for robust uninhabited air vehicle active missions.
This patent application is currently assigned to Lockheed Martin Corporation. Invention is credited to Stephen Francis Bush.
Application Number | 20070061116 09/994447 |
Document ID | / |
Family ID | 37856382 |
Filed Date | 2007-03-15 |
United States Patent
Application |
20070061116 |
Kind Code |
A1 |
Bush; Stephen Francis |
March 15, 2007 |
ROBUST UNINHABITED AIR VEHICLE ACTIVE MISSIONS
Abstract
A command sequence for an autonomous UAV mission is optimized by
simulating the performance of a mission in a model environment.
Using a genetic algorithm, neural net, or other suitable technique
this command sequence is then optimized, to improve the outcome of
the mission. A factor in selecting an optimal command sequence will
be its compressability. A set of one or more optimal command
sequences is compiled. Each optimal command sequence is encoded
into an algorithmic active packet of minimum size for uploaded to
the UAV, which then executes the mission. To track the UAV in its
performance of the mission without compromising its location, the
active packets are executed in the simulated environment. The
simulated environment is continually updated with the most current
available information. The simulation results are an approximation
of the current state of the UAV.
Inventors: |
Bush; Stephen Francis;
(Latham, NY) |
Correspondence
Address: |
Paul J. Esatto, Jr.;Scully, Scott, Murphy & Presser
400 Garden City Plaza
Garden City
NY
11530
US
|
Assignee: |
Lockheed Martin Corporation
Bethesda
MD
|
Family ID: |
37856382 |
Appl. No.: |
09/994447 |
Filed: |
November 27, 2001 |
Current U.S.
Class: |
703/8 |
Current CPC
Class: |
G01C 23/00 20130101;
B64C 2201/141 20130101 |
Class at
Publication: |
703/008 |
International
Class: |
G06G 7/48 20060101
G06G007/48 |
Claims
1. A method of optimizing a command sequence for a UAV to
accomplish mission objectives, comprising the steps of: (a)
simulating the performance of an initial command sequence by a UAV
in a simulated environment, resulting in a simulated mission
outcome; (b) modifying the command sequence of said mission; (c)
simulating the performance of said modified command sequence by a
UAV in said simulated environment, resulting in another simulated
mission outcome; (d) iteratively performing steps (b) and (c) to
optimize the simulated mission outcome; (e) selecting the one or
more command sequences based in part upon which command sequences
produce an optimal simulated mission outcome; and (f) encoding each
selected command sequence into an algorithmic active packet.
2. The method of optimizing a command sequence for a UAV according
to claim 1, wherein modifying the command sequence comprises using
one of a genetic algorithm technique and a neural network
technique.
3. The method of optimizing a command sequence for a UAV according
to claim 2, wherein modifying the command sequence comprises using
a genetic algorithm technique, and further wherein said genetic
algorithm comprises a fitness function which measures the simulated
outcome against mission objectives.
4. The method of optimizing a command sequence for a UAV according
to claim 1, wherein the criteria for an optimal mission outcome
include the compressibility of the command sequence.
5. The method of optimizing a command sequence for a UAV according
to claim 4, wherein the compressibility of the command sequence is
measured according to the Minimum Data Length theorem.
6. The method of optimizing a command sequence for a UAV according
to claim 1, wherein step of encoding a command sequence includes
representing the commands as an algorithm supplemented by data.
7. The method of optimizing a command sequence for a UAV according
to claim 6, wherein the encoded command sequence achieves an
optimal compression as measured by the Minimum Data Length
theorem.
8. A method of tracking an autonomous UAV during the performance of
a pre-programmed active mission, comprising the steps of: (a)
simulating the performance of the active mission programmed into
the UAV in a current simulation of the environment the UAV is
operating in; and (b) estimating the present position of the UAV
based upon the results of the simulation.
Description
Background of the Invention
[0001] 1. Field of Invention
[0002] The invention relates generally to the field of Uninhabited
Air Vehicles (UAVs), and more particularly, it relates to a method
of training and monitoring a UAV for a specific mission.
[0003] 2. Description of Related Art
[0004] Autonomous unmanned air vehicles (UAV) have great potential
for military and civilian use. Clearly, intelligent unmanned
vehicles can readily be sent into hostile situations without fear
of casualties. In addition, because the aircraft is intelligent,
communication with the vehicle is unnecessary thus increasing its
undetected surveillance capability.
[0005] Current UAVs have not met the degree of safety and
reliability required for autonomous operation over populated areas
or in airspace shared with commercial aircraft. Autonomy
technologies that can provide reflexive responses and rapid
adaptation (as exhibited by a pilot) to compensate for a vehicle's
structural, perceptual and control limitations are lacking. This is
particularly evident when UAV mishap rates are compared to those of
piloted systems.
[0006] Compared to piloted aircraft systems, current UAVs are
designed to be very low cost, use smaller low-power commercial
off-the-shelf components and have very limited redundancy.
Unfortunately, the lower requirement for reliability has led to
higher failure rates. The higher failure rate is seen as somewhat
acceptable because it does not mean the loss of human life, except
when the vehicle flies over populated areas. It is desirable,
however, for a UAV to be able to safely fly over populated areas,
to safely share airspace with other piloted vehicles, and to
generally improve the mission success rate. For these reasons, the
UAV control systems must be capable of rigorously analyzing and
predicting component failures and their effects to determine the
appropriate response to faults much as a pilot does prior to or as
a result of system failure.
BRIEF SUMMARY OF THE INVENTION
[0007] The present invention includes providing a simulation of the
environment the UAV is to operate in, and simulating the
performance of a mission by the UAV. This simulation takes into
account environmental stimuli and mission objectives, and outputs
some mission outcome. The command sequence is then optimized using
a genetic algorithm, neural net, or other suitable technique, to
improve the outcome of the mission. A set of one or more optimal
command sequences to achieve the mission is compiled, and each
optimal command sequence is encoded into an algorithmic active
packet of minimum size. An active packet is the object communicated
in an active network. Active networks are a recent development in
computer science and networking technology. The application of
active networking to the present invention will be elaborated,
infra. These active packets are uploaded to the UAV, which then
executes the mission.
[0008] To track the UAV in its performance of the mission without
compromising its location, the active packets are executed in the
simulated environment. The simulated environment is continually
updated with the most current available information. The simulation
results are an approximation of the current state of the UAV.
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] These and other features, aspects and advantages of the
present invention will be apparent from the following drawings,
description and appended claims, where:
[0010] FIGS. 1A and 1B, bridged by connector A, represent a flow
chart of an exemplary embodiment of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
[0011] It is desirable for a UAV operating over hostile territory
to be undetectable. Towards that end, limiting or eliminating radio
transmissions to and from the UAV decreases the likelihood of
detection. Therefore, a UAV capable of operating autonomously
without the need to report its status to a remote control system
and receive commands from it is less detectable. Further, an
autonomous UAV is not vulnerable to having its commands overridden
by an outside source.
[0012] In order to achieve this goal of autonomy, a UAV must
incorporate all decision making into the vehicle while executing a
mission. One question that arises is how to best communicate the
mission to the UAV. The mission may be represented by static
waypoints and commands. However, it can be more efficient to
represent the mission in a programmatic or algorithmic manner.
[0013] The co-pending application "Optimistic Distributed
Simulation for a UAV Flight Control System", Ser. No. ______
(unassigned, attorney docket no. 14874), hereby incorporated by
reference, is directed toward active network control of a UAV.
Active network control includes state objects that comprise
executable code to process the control model. The active missions
of the present invention define the executable code for a given UAV
mission.
[0014] Referring now to FIG. 1A, in an exemplary embodiment, the
method of the present invention, generally 100, begins 102 by
preparing a simulation 104 of the environment the UAV is to operate
in. The simulated environment could include topographical terrain
information, known weather conditions and their predicted
movements, and/or known enemy locations.
[0015] Additionally in preparation, the mission objectives must be
defined 106. In one illustration, a reconnaissance mission has the
objectives to pass through a given waypoint, take a photograph, and
return to base.
[0016] A simplistic model of this mission would be a set of
intermediate waypoints associated with commands to be executed at
those waypoints. The waypoints trace the course of the mission, and
the commands specify the actions the UAV will take to achieve the
mission at each waypoint. For example, the instruction at an
intermediate waypoint may be a null, i.e., an instruction to take
no action. The instruction at the target waypoint could be to take
a picture.
[0017] A randomized, though feasible, command sequence is initially
generated 108. A feasible command sequence is one that can achieve
the mission goals, and is within the capabilities of the UAV. For
example, a next waypoint that cannot be reached by the UAV, either
because of a turn radius that is impossible to achieve or because
it is beyond the operating range of the UAV, is unfeasible. The
initial command sequence is simulated 110, and the outcome is
evaluated 112, for example against a fitness function.
[0018] When using a genetic algorithm as part of the optimization
according to the present method, a fitness function is defined, in
a manner known in the art. In this case, the fitness function
measures the outcome of the UAV simulation of the command sequence.
The fitness function consists of measurable objectives towards
achieving the mission goal. An example fitness function for this
sample mission might include the following elements: TABLE-US-00001
TABLE Fitness Function Elements Measurable damage to the UAV, with
emphasis on the flight capability and whether the camera remains in
an operational state (minimize damage) The minimum distance
ultimately reached by the UAV from the target to be photographed
(minimize target error) The minimum distance of the UAV from base
after the target has been photographed and begins the return flight
(minimize return error) Estimated complexity of the command
sequences generated based upon Minimum Data Length (MDL) theory
(minimize complexity)
[0019] The evaluation of the outcome is compared against some
threshold value 114, to determine if more modification 116 is
necessary. Care must be taken to avoid converging on a local,
rather than global, minimum or maximum value of the fitness
function. Through iterative simulation, an optimal command sequence
to achieve the mission is developed.
[0020] Continuing with example of the genetic algorithm procedure,
parent selection, mating and mutation are then performed to
optimize the outcome according to the fitness function. Again, this
genetic algorithm technique is known in the art, and need not be
discussed further. See Schatten, A., Genetic Algorithm Short
Tutorial,
http://www.ifs.tuwien.ac.at/.about.aschatt/info/ga/genetic.html,
which is hereby incorporated by reference.
[0021] The genetic algorithm will evolve a command sequence
optimized to the fitness function. For example, an elevation at a
given waypoint may be increased to move above the range of enemy
fire. Alternately, the elevation may be reduced to mask the UAV
behind terrain features. It is possible that more than one command
sequence will result in an optimal mission outcome.
[0022] Though the genetic algorithm is illustrated for educing an
optimal command sequence, it is not the exclusive means of
accomplishing this task. Neural networks techniques, for example,
are also well suited to the method of the present invention.
[0023] At least one element of the preset invention is including
the compressability of the command sequence as a criterion on the
same level as an objective of the mission. Its influence will be
arbitrary with the relative weighting of the objectives, but this
will allow the process to converge, not only on an optimal result,
but also on a result that can be optimally communicated to the
UAV.
[0024] Referring now to FIG. 1B, in the next step of the present
method, a set comprising one or more optimal command sequences will
be compressed 118 for efficient upload 120 to the UAV. Consider a
command sequence as a bound string, x. The Kolmogorov Complexity
Estimation, s, K(x), is the theoretical optimal compression of
bound string x. Bound sting x will contain some non-random data
that can be expressed algorithmically as code, and some random data
that must be expressed as data. The optimal balance of code and
data is the subject of the Minimum Data Length (MDL) theorem. See
Wallace, C. S., and Dowe, D. L., Minimum Message Length and
Kolmogorov Complexity, The Computer Journal, Vol. 42, No. 4,
1999.
[0025] MDL states that the sum of the length of the hypothesis
(L.sub.H) about the model generating bound string x and the length
of the string (L.sub.D) encoded by this hypothesis will estimate
the Kolmogorov Complexity of the string, according to the equation:
K(x).apprxeq.L.sub.H+L.sub.D
[0026] Using MDL, efficiency of the command sequence's
representation as an active packet can be measured. The hypothesis
predicts the value of x, and the data corrects for inaccuracy in
the hypothesis due to randomness of the sequence. At a most basic
level, the command sequence may be compressed according to any
well-known data compression algorithm. However, specific knowledge
of the data to be compressed allows a more efficient hypothesis to
be developed.
[0027] As an illustration, the waypoints defining the course of the
sample mission, supra, may be represented by a curve fit. The
defining curve is a much more efficient representation of the
course than individual waypoints. This information can be
represented as code. However, the point at which a picture is to be
taken is likely random. It would not be possible to represent this
information algorithmically. Therefore, the command to photograph
would form the data portion of the active packet, while the course
would form the algorithmic portion.
[0028] Once the command sequences are compressed 118 into an active
packet of minimum size, they can be efficiently uploaded 120 to the
UAV. In an effort to make the UAV completely autonomous, this would
take place before the UAV is launched. However, another advantage
of the present invention is that the active packet may be uploaded
by transmission to a UAV already in flight, while minimizing the
risk by minimizing the transmission length compared to raw data
mission commands.
[0029] Tracking the progress of the UAV on the mission has begun
122 by having the UAV transmit status messages could compromise its
safety. It is, however, desirable to know when the UAV is or is
likely to be during the performance of the mission. Again,
referring to the co-pending application "Optimistic Distributed
Simulation for a UAV Flight Control System", we assume that control
of the UAV while in the performance of the mission includes some
ability to adapt to variables than cannot be predicted. Once these
conditions become known, however, they can be input into the
simulation to determine how the UAV would react in performance of
the previously defined mission.
[0030] In order to track the UAV 124, the active packets are
executed in the simulated environment. If the simulated environment
is continually updated with the most current information, then the
simulation results will be a good approximation of the state and
location of the UAV in performing its mission. The tracking is
continuous 126 until the mission is complete 128.
[0031] The invention has been described herein with reference to
particular exemplary embodiments. Certain alterations and
modifications may be apparent to those skilled in the art, without
departing from the scope of the invention. The exemplary
embodiments are not meant to be limiting on the scope of the
invention, which is defined by the appended claims.
* * * * *
References