U.S. patent application number 12/711935 was filed with the patent office on 2010-09-09 for predictive semi-autonomous vehicle navigation system.
This patent application is currently assigned to MASSACHUSETTS INSTITUTE OF TECHNOLOGY. Invention is credited to Sterling J. Anderson, Karl D. Iagnemma, Steven C. Peters.
Application Number | 20100228427 12/711935 |
Document ID | / |
Family ID | 42678956 |
Filed Date | 2010-09-09 |
United States Patent
Application |
20100228427 |
Kind Code |
A1 |
Anderson; Sterling J. ; et
al. |
September 9, 2010 |
PREDICTIVE SEMI-AUTONOMOUS VEHICLE NAVIGATION SYSTEM
Abstract
An active safety framework performs trajectory planning, threat
assessment, and semi-autonomous control of passenger vehicles in a
unified, optimal fashion. The vehicle navigation task is formulated
as a constrained optimal control problem. A predictive, model-based
controller iteratively plans an optimal or best-case vehicle
trajectory through the constrained corridor. This best-case
scenario is used to establish the minimum threat posed to the
vehicle given its current state, current and past driver
inputs/performance, and environmental conditions. Based on this
threat assessment, the level of controller intervention required to
prevent collisions or instability is calculated and
driver/controller inputs are scaled accordingly. This approach
minimizes controller intervention while ensuring that the vehicle
does not depart from a traversable corridor. It also provides a
unified architecture into which various vehicle models, actuation
modes, trajectory-planning objectives, driver preferences, and
levels of autonomy can be seamlessly integrated without changing
the underlying controller structure.
Inventors: |
Anderson; Sterling J.;
(Allston, MA) ; Peters; Steven C.; (Cambridge,
MA) ; Iagnemma; Karl D.; (Cambridge, MA) |
Correspondence
Address: |
STEVEN J WEISSBURG
190 WALDEN STREET, 3RD FLOOR
CAMBRIDGE
MA
02140
US
|
Assignee: |
MASSACHUSETTS INSTITUTE OF
TECHNOLOGY
Cambridge
MA
|
Family ID: |
42678956 |
Appl. No.: |
12/711935 |
Filed: |
February 24, 2010 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61209250 |
Mar 5, 2009 |
|
|
|
Current U.S.
Class: |
701/31.4 ;
701/1 |
Current CPC
Class: |
G08G 1/166 20130101;
B60W 50/0098 20130101; B60W 30/09 20130101; G08G 1/165 20130101;
B60W 50/10 20130101; G05D 1/00 20130101; G05D 1/0088 20130101 |
Class at
Publication: |
701/29 ;
701/1 |
International
Class: |
G06F 7/00 20060101
G06F007/00 |
Claims
1. A method for generating a set of machine control inputs for
semi-autonomously controlling a vehicle operating in an
environment, with a variable degree of human operator control
relative to the degree of machine control, the method comprising
the steps of: a. predicting an optimal vehicle trajectory from a
current position through a time horizon: b. assessing a predicted
threat to the vehicle and generating a corresponding threat metric;
c. based on the threat metric, generating at least one control
authority gain; and d. generating at least one machine control
optimal input; and e. generating at least one machine control
scaled input, based on the machine control optimal input and the
control authority gain; whereby the degree of machine control of
the vehicle relative to the degree of human operator control of the
vehicle varies depending on the control authority gain.
2. The method of claim 1, the step of predicting an optimal vehicle
trajectory being based on: a. a model of the environment; b. a
model of the vehicle; c. the vehicle's current state; d. driver
inputs; and e. a corresponding optimal set of control inputs;
3. The method of claim 1, the step of assessing a predicted threat
being based on: a. characteristics of optimal vehicle path and
associated control input; b. environmentally imposed safety
constraints; and c. and driver inputs.
4. The method of claim 1, the step of generating at least one
machine control scaled input being based on an intervention
characteristic.
5. The method of claim 4, the intervention characteristic being
chosen from the group consisting of a linear function of current
and past predicted threat, and current and past control input; and
a nonlinear function of current and past predicted threat, and
current and past control input.
6. The method of claim 1, the environmental model being based on a
priori known information.
7. The method of claim 1, the environmental model being based on
information gathered by real-time sensors.
8. The method of claim 1, the threat metric being at least one
metric selected from the group consisting of: maximum lateral
acceleration, sideslip angle, roll angle over the trajectory and a
minimum proximity to obstacles.
9. The method of claim 1, the optimal vehicle trajectory and
associated optimal control inputs being computed by constrained
optimal control.
10. The method of claim 3, the vehicle comprising an automotive
vehicle, with at least one sensor generating data related to at
least one of the factors in the group consisting of: nearby
vehicles, pedestrians, road edges, roadway hazards, road surface
friction and other environmental characteristics.
11. The method of claim 1, control authority gain being such that
if the threat metric value is: a. low, the control system
intervention is low and thus, the human operator controls the
vehicle with minimal computer-controlled intervention); and b.
high, the control system intervention is high and thus, the human
operator controls the vehicle with significant computer controlled
intervention.
12. The method of claim 1, the vehicle comprising an automotive
vehicle, at least one machine control scaled input being selected
from the group consisting of: steering, braking and
acceleration.
13. The method of claim 1, the optimal set of machine control
inputs used in the step of predicting an optimal vehicle trajectory
comprising machine control inputs having been generated by the
method for generating a set of automated control inputs.
14. An apparatus for use of generating a set of machine control
inputs thereby controlling a vehicle operating in an environment,
with a variable degree of human operator control and a variable
degree of machine control, the apparatus comprising: a. means for
predicting an optimal safe vehicle trajectory from a current
position through a time horizon based on; i. a model of the
environment; ii. a model of the vehicle; iii. the vehicle's current
state; iv. driver inputs; and v. a corresponding optimal set of
control inputs; b. means for assessing a predicted threat to the
vehicle and generating a corresponding threat metric; c. a machine
controller that generates at least one machine control optimal
input; d. means for generating at least one control authority gain
based on the threat metric; e. means for generating at least one
machine control scaled input based on the at least one machine
control optimal input and the control authority gain; f. means for
generating a scaled human operator input, based on a human operator
command, and the control authority, whereby the human operator
scaled input is also based on the control authority gain, inversely
to the degree that the machine control scaled input is based on the
at least one machine control optimal input; and g. an input
combiner, which combines the human operator scaled input and the
machine control scaled input to an actuator that actuates a system
of the vehicle.
15. An automotive vehicle having a chassis, wheels, a power plant,
a body, and a control apparatus, the control apparatus generating a
set of machine control inputs thereby controlling the vehicle while
operating in an environment, with a variable degree of human
operator control and a variable degree of machine control the
control apparatus comprising: a. means for predicting an optimal
safe vehicle trajectory from a current position through a time
horizon based on; i. a model of the environment; ii. a model of the
vehicle; iii. the vehicle's current state; iv. driver inputs; and
v. a corresponding optimal set of control inputs; b. means for
assessing a predicted threat to the vehicle and generating a
corresponding threat metric; c. a machine controller that generates
at least one optimal machine control input; d. means for generating
at least one control authority gain based on the threat metric; e.
means for generating at least one machine control scaled input
based on the at least one machine control optimal input and the
control authority gain; f. means for generating a scaled human
operator input, based on a human operator command, and the control
authority, whereby the human operator scaled input is also based on
the control authority gain, inversely to the degree that the
machine control scaled input is based on the at least one optimal
control input; and g. an input combiner, which combines the human
operator scaled input and the machine control scaled input to an
actuator that actuates a system of the vehicle.
16. The method of claim 1, the threat metric being at least one
metric selected from the group consisting of: characteristics of
the optimal vehicle path and control input, including predicted
vehicle states of lateral acceleration, vehicle sideslip angle,
tire sideslip angle, road friction utilization, roll angle, pitch
angle, past and present driver performance, environmentally-imposed
safety constraints, and proximity to hazards.
17. The method of claim 16, the threat metric being based on at
least one metric selected from the group consisting of: average,
maximum, minimum, and RMS norms of a predicted vehicle state.
18. The method of claim 17, the predicted vehicle state being
selected from the group consisting of: lateral acceleration,
vehicle sideslip angle, tire sideslip angle, road friction
utilization, roll angle, pitch angle, driver inputs, and proximity
to hazards.
Description
RELATED DOCUMENT
[0001] Priority is hereby claimed to U.S. Provisional application
Ser. No. 61/209,250, entitled PREDICTIVE SEMI-AUTONOMOUS VEHICLE
NAVIGATION SYSTEM, in the names of Sterling J. Anderson, Steven C.
Peters and Karl D. Iagnemma, filed on Mar. 5, 2009, which is hereby
fully incorporated herein by reference.
BRIEF DESCRIPTION OF THE FIGURES OF THE DRAWING
[0002] FIG. 1 is a block diagram illustrating basic framework
operation.
[0003] FIG. 2 graphically shows an example of various potential
intervention laws based on threat metric calculation.
[0004] FIG. 3 graphically shows an obstacle avoidance scenario
illustrating different stages of intervention for an inattentive
driver.
[0005] FIG. 4 shows, in flowchart form, a basic algorithm logic
flow with possible considerations at each step.
[0006] FIG. 5 shows, graphically, a simulated test illustrating
system response when driver fails to navigate a curve in the road.
K represents the proportion of control authority given to
autonomous system, with the driver allowed the remaining (1-K).
[0007] FIG. 6 shows, graphically, a simulated test illustrating
system response to an erroneous driver swerve, where K represents
the proportion of control authority given to autonomous system,
with the driver allowed the remaining (1-K).
[0008] FIG. 7 shows, graphically, a simulated test illustrating
system response when driver fails to anticipate/avoid obstacle. K
represents the proportion of control authority given to autonomous
system, with the driver allowed the remaining (1-K).
DETAILED DESCRIPTION
[0009] Inventions described herein relate to a unified framework
for performing threat assessment and semi-autonomous vehicle
navigation and control while allowing for adaptable and
configurable intervention laws and configurable control inputs.
[0010] Automotive active safety systems are concerned with
preventing accidents through the introduction of various
computer-controlled actuation methods to improve driver braking and
steering performance. Current active safety systems include yaw
stability control, roll stability control, traction control, and
antilock braking, among others. While these systems reduce accident
frequency, they are fundamentally reactive in nature: their
intervention is based on current vehicle (and, possibly, road
surface) conditions. Because they do not utilize 1) sensory
information related to the vehicle surroundings or 2) a prediction
of the vehicle's path through its surroundings, they have limited
ability to assess the threat of impending accidents, and thus
cannot exert corrective actions to avoid them.
[0011] Active navigation systems, such as the one described here,
aim to avoid accidents by utilizing sensory information related to
the vehicle surroundings and a prediction of a safe vehicle
trajectory through those surroundings to exert appropriate actuator
effort to avoid impending accidents. Sensory information would
include data related to nearby vehicles, pedestrians, road edges,
and other salient features to assess accident threat.
[0012] Such navigation systems ideally operate only during
instances of significant threat: it should give a driver full
control of the vehicle in low threat situations but apply
appropriate levels of computer-controlled actuator effort during
high threat situations. An active navigation system can therefore
be termed semi-autonomous, since it must allow for
human-controlled, computer-controlled, and shared human/computer
vehicle operation. Such a system should be as unobtrusive to the
driver as possible (i.e. it should intervene only as much as is
minimally required to avoid an impending accident).
[0013] The semi-autonomous active navigation system described here
satisfies all of the above requirements and desired
characteristics. Further, it provides a framework into which
various distinct sensing and actuation modes can be easily
incorporated. The system's method for threat assessment and
computer-controlled intervention can potentially be modified in
real time based on the scenario, environmental conditions, driver
preference, or past driver performance. FIG. 1 shows,
schematically, in block diagram form, a basic framework
operation.
[0014] This semi-autonomous vehicle navigation system predicts an
optimal (with respect to a pre-defined, configurable set of
criteria) vehicle trajectory from the current position through a
finite time horizon given a model of the environment, a model of
the vehicle, the vehicle's current state, and a corresponding
optimal set of control inputs (also calculated by the system). The
environment model can be based on a priori known information (e.g.
from maps) and/or information gathered by real time sensors, such
as on-vehicle sensors (e.g. cameras and laser rangefinders), and
can include information related to the position of road edges,
static obstacles (e.g. trees, road-side signs), and dynamic
obstacles (e.g. other vehicles, pedestrians). The vehicle model is
user-defined and can be of varying complexity and fidelity. The
real-time sensors may also be mounted in the environment and
communicate with the control system on the vehicle.
[0015] The predicted safe vehicle trajectory (and associated
control inputs to yield such a trajectory) is found such that it
satisfies a configurable set of trajectory requirements, including,
for example, that the vehicle position remain within a safe driving
corridor, that the vehicle sideslip angle not exceed the safe limit
of vehicle handling, that tire friction forces not exceed a surface
friction-limited value, and others. The control inputs can be
associated with one or multiple actuators, such as active steering,
active braking, and others. The predicted vehicle trajectory and
associated control inputs may be computed via constrained optimal
control, which leverages efficient optimization methods and
constraint-handling capabilities.
[0016] At successive discrete sampling instants, the predicted
vehicle trajectory and control inputs are analyzed to assess the
threat to the vehicle by computing a configurable metric, such as
the maximum lateral acceleration, sideslip angle, or roll angle
over the trajectory, the minimum proximity to obstacles, or others.
The control authority exerted by the system is then determined as a
function of this computed threat: generally speaking, if the threat
metric value is low, the control system intervention is low (i.e.
the driver commands the vehicle with little or no
computer-controlled intervention); if the threat metric value is
high, the control system intervention is high. The form of the
intervention law modulating this control system authority is
configurable and can differ for different actuators (i.e. a vehicle
with both active steering and braking can have distinct
intervention laws defined for the steering actuator and the braking
actuators). The intervention law can also be defined to adapt to
driver performance based on an assessment of driver skill, and/or
to include considerations for driver preference, environmental
conditions, previous threat metric values, previous control inputs,
and other factors. FIG. 2 shows, schematically, examples of various
potential intervention laws, showing, from top to bottom, linear,
smooth and threshold-shaped intervention laws that depend only on
predicted threat. The vertical axis represents the degree of
control authority given to the active navigation and control system
while the horizontal axis represents the predicted threat, with
cause for intervention increasing from left to right.
[0017] In the system described above, as the threat metric value
increases, indicating that the predicted vehicle trajectory will
near a pre-defined critical vehicle state(s) (such as spatial
location, lateral acceleration, or tire friction saturation), the
control system begins to assume control authority to preempt an
unsafe maneuver. As the threat metric decreases, the controller's
authority phases out. In this manner, the system can said to be
semi-autonomous.
[0018] Note that in extreme cases, when the driver does not perform
an appropriate corrective action, it is conceivable that a required
hazard avoidance maneuver will reach vehicle handling limits. To
account for such scenarios, the intervention law can be designed
such that it assumes full authority by the time the predicted safe
trajectory reaches the limit of any pre-defined critical vehicle
states. This corresponds to a situation where only an optimal set
of inputs would result in a safe vehicle trajectory.
[0019] FIG. 3 shows schematically an obstacle avoidance scenario
illustrating different stages of intervention for an inattentive
driver. FIG. 4 shows, schematically, in flow chart form, a basic
flow of logic performed by a controller of an invention hereof,
with possible considerations at each step.
[0020] An initial step calculates an optimal set of control inputs
and corresponding vehicle trajectory. Considerations for this step
include, for example, (but are not limited to) the vehicle
dynamics, current state of the vehicle and environment, terrain and
environmental disturbances, available actuation, trajectory
objectives, safety limits, and driver inputs.
[0021] A next step is to assess the predicted threat to the
vehicle. Considerations for this step include characteristics of
the optimal path and associated control input, safety limits and
driver inputs. A next step is to calculate control authority gains,
with a major consideration at this stage being the desired
intervention characteristic. The next step is to implement the
scaled control for the current time.
[0022] Simulation experiments have been conducted. FIG. 5 shows,
graphically, the results of a simulated test illustrating system
response when a driver fails to navigate a curve in the road, shown
by a light gray line. The trajectory that the driver would have
followed without assistance is shown dashed. With assistance, it is
shown solid black. Note that in this embodiment of the invention, K
represents proportion of control authority given to the autonomous
system, with the driver allowed the remaining (1-K). The middle
graph shows the steer inputs, with the dashed line corresponding to
the driver and the solid curve corresponding to the control system.
The lower graph shows the control authority given to the autonomous
system, in this case, steering, with the degree varying with
distance (x) along the horizontal scale.
[0023] FIG. 6 shows, graphically, the results of a simulated test
illustrating the system response to an erroneous driver swerve.
Again, K represents proportion of control authority given to
autonomous system, with the driver allowed the remaining (1-K). The
same line types as above correspond to the driver without
assistance (gray dashed) and with assistance (solid line). The safe
roadway is shown in light gray solid lines in the upper graph.
Distance is shown along the horizontal scale.
[0024] FIG. 7 shows, graphically, a simulated test illustrating
system response when a driver fails to anticipate/avoid an
obstacle. Again, K represents the proportion of control authority
given to autonomous system. The obstacle is simulated by a jog in
the light gray line that represents the safe roadway. The only
inputs used in this simulation are, again, steering of the driver
and the autonomous system.
[0025] Significant advantages stem from the predictive nature of
this solution. In addition to considering past and current vehicle
and driver actions to assess threat and determine control
authority, the current solution predicts a future vehicle
trajectory and associated threat, and uses this prediction to
schedule control authority.
[0026] This predictive nature also allows for a more accurate
assessment of threat than is otherwise possible. While other threat
assessment metrics rely on highly simplified physics-based
calculations, the metrics used in the current solution can derive
from sophisticated vehicle and environmental models. These models
yield more accurate threat assessments by considering the effects
of terrain conditions, environmental disturbances, and physical
limitations of vehicle actuators. These models can also assess
threat for more complex vehicle trajectories than is possible with
simplified models.
[0027] Finally, this system provides improved modularity and
adaptability when compared to previous solutions. Its underlying
control framework can accommodate multiple actuation modes and
vehicle models, allowing for ready application of the system to
various vehicle types and actuator configurations. The system's
intervention law is also readily adapted (i.e. it can change over
time based on an assessment of driver skill, driver preference,
environmental conditions, previous threat metric values, previous
control inputs, and other factors). These adaptations can be
performed either statically or dynamically.
SUMMARY
[0028] An important aspect of inventions disclosed herein is a
method for generating a set of machine control inputs for
semi-autonomously controlling a vehicle operating in an
environment, with a variable degree of human operator control
relative to the degree of machine control. The method comprises the
steps of: predicting an optimal vehicle trajectory from a current
position through a time horizon; assessing a predicted threat to
the vehicle and generating a corresponding threat metric; based on
the threat metric, generating at least one control authority gain;
and generating at least one machine control optimal input; and
generating at least one machine control scaled input, based on the
machine control optimal input and the control authority gain. In
this manner, the degree of machine control of the vehicle relative
to the degree of human operator control of the vehicle varies
depending on the control authority gain.
[0029] With a closely related method, the step of predicting an
optimal vehicle trajectory is based on: a model of the environment;
a model of the vehicle; the vehicle's current state; driver inputs;
and a corresponding optimal set of control inputs.
[0030] For another important related method, the step of assessing
a predicted threat is based on: characteristics of optimal vehicle
path and associated control input; environmentally imposed safety
constraints; and driver inputs.
[0031] Still another important aspect has the step of generating at
least one machine control scaled input being based on an
intervention characteristic. In such a case, the intervention
characteristic may be chosen from the group consisting of: a linear
function of current and past predicted threat, and current and past
control input; and a nonlinear function of current and past
predicted threat, and current and past control input.
[0032] The environmental model may be based on a priori known
information.
[0033] Or, the environmental model may be based on information
gathered by real-time sensors.
[0034] Another interesting embodiment has the threat metric being
at least one metric selected from the group consisting of: maximum
lateral acceleration, sideslip angle, roll angle over the
trajectory and a minimum proximity to obstacles.
[0035] Another aspect of an invention hereof has a threat metric
being at least one metric selected from the group consisting of:
characteristics of the optimal vehicle path and control input,
including predicted vehicle states such as lateral acceleration,
vehicle sideslip angle, tire sideslip angle, road friction
utilization, roll angle, pitch angle, past and present driver
performance, environmentally-imposed safety constraints, and
proximity to hazards. For a specific aspect, the threat metric may
be at least one metric based on one the group consisting of:
average, maximum, minimum, and RMS norms of a predicted vehicle
state. In such a case, the predicted vehicle state may be selected
from the group consisting of: lateral acceleration, vehicle
sideslip angle, tire sideslip angle, road friction utilization,
roll angle, pitch angle, driver inputs, and proximity to
hazards.
[0036] For a related embodiment, the optimal vehicle trajectory and
associated optimal control inputs can be computed by constrained
optimal control.
[0037] The vehicle may be an automotive vehicle, with at least one
sensor generating data related to at least one of the factors in
the group consisting of: nearby vehicles, pedestrians, road edges,
roadway hazards, road surface friction and other environmental
characteristics.
[0038] With most such methods, the control authority gain may be
such that if the threat metric value is low, the control system
intervention is low and thus, the human operator controls the
vehicle with minimal computer-controlled intervention). Conversely,
if the threat metric value is high, the control system intervention
is high and thus, the human operator controls the vehicle with
significant computer controlled intervention.
[0039] For a very useful embodiment, the vehicle may comprise an
automotive vehicle, where at least one machine control scaled input
is selected from the group consisting of: steering, braking and
acceleration.
[0040] For many related embodiments, the optimal set of machine
control inputs used in the step of predicting an optimal vehicle
trajectory may comprise machine control inputs having been
generated by the method for generating a set of automated control
inputs.
[0041] Another aspect of inventions disclosed herein is an
apparatus for generating a set of machine control inputs, thereby
controlling a vehicle operating in an environment, with a variable
degree of human operator control and a variable degree of machine
control. The apparatus comprises: a. means for predicting an
optimal safe vehicle trajectory from a current position through a
time horizon, based on: a model of the environment; a model of the
vehicle; the vehicle's current state; driver inputs; and a
corresponding optimal set of control inputs. This aspect of the
inventions further includes: b. means for assessing a predicted
threat to the vehicle and generating a corresponding threat metric;
c. a machine controller that generates at least one machine control
optimal input; d. means for generating at least one control
authority gain based on the threat metric; e. means for generating
at least one machine control scaled input based on the at least one
machine control optimal input and the control authority gain; f.
means for generating a scaled human operator input, based on a
human operator command, and the control authority, whereby the
human operator scaled input is also based on the control authority
gain, inversely to the degree that the machine control scaled input
is based on the at least one machine control optimal input; and g.
an input combiner, which combines the human operator scaled input
and the machine control scaled input to an actuator that actuates a
system of the vehicle.
[0042] Another basic aspect of inventions hereof is an automotive
vehicle having a chassis, wheels, a power plant, a body, and a
control apparatus, the control apparatus generating a set of
machine control inputs, thereby controlling the vehicle while
operating in an environment, with a variable degree of human
operator control and a variable degree of machine control. The
control apparatus comprises: a. means for predicting an optimal
safe vehicle trajectory from a current position through a finite
time horizon based on: a model of the environment; a model of the
vehicle; the vehicle's current state; driver inputs; and a
corresponding optimal set of control inputs. The control apparatus
further comprises: b. means for assessing a predicted threat to the
vehicle and generating a corresponding threat metric; c. a machine
controller that generates at least one optimal machine control
input; d. means for generating at least one control authority gain
based on the threat metric; e. means for generating at least one
machine control scaled input based on the at least one machine
control optimal input and the control authority gain; f. means for
generating a scaled human operator input, based on a human operator
command, and the control authority, whereby the human operator
scaled input is also based on the control authority gain, inversely
to the degree that the machine control scaled input is based on the
at least one optimal control input; and g. an input combiner, which
combines the human operator scaled input and the machine control
scaled input to an actuator that actuates a system of the
vehicle.
[0043] This disclosure describes and discloses more than one
invention. The inventions are set forth in the claims of this and
related documents, not only as filed, but also as developed during
prosecution of any patent application based on this disclosure. The
inventors intend to claim all of the various inventions to the
limits permitted by the prior art, as it is subsequently determined
to be. No feature described herein is essential to each invention
disclosed herein. Thus, the inventors intend that no features
described herein, but not claimed in any particular claim of any
patent based on this disclosure, should be incorporated into any
such claim.
[0044] Some assemblies of hardware, or groups of steps, are
referred to herein as an invention. However, this is not an
admission that any such assemblies or groups are necessarily
patentably distinct inventions, particularly as contemplated by
laws and regulations regarding the number of inventions that will
be examined in one patent application, or unity of invention. It is
intended to be a short way of saying an embodiment of an
invention.
[0045] An abstract is submitted herewith. It is emphasized that
this abstract is being provided to comply with the rule requiring
an abstract that will allow examiners and other searchers to
quickly ascertain the subject matter of the technical disclosure.
It is submitted with the understanding that it will not be used to
interpret or limit the scope or meaning of the claims, as promised
by the Patent Office's rule.
[0046] The foregoing discussion should be understood as
illustrative and should not be considered to be limiting in any
sense. While the inventions have been particularly shown and
described with references to preferred embodiments thereof, it will
be understood by those skilled in the art that various changes in
form and details may be made therein without departing from the
spirit and scope of the inventions as defined by the claims.
[0047] The corresponding structures, materials, acts and
equivalents of all means or step plus function elements in the
claims below are intended to include any structure, material, or
acts for performing the functions in combination with other claimed
elements as specifically claimed.
* * * * *