Transcript Slide 1

Vehicle Autonomy and Intelligent Control
J. A. Farrell
Department of Electrical Engineering
University of California, Riverside
Intelligence & Autonomy
Increasingly capable autonomous
vehicles: a worthy challenge
necessitating increased ability along
various dimensions of intelligence
Value
Judgment
Sensor
World
Processing Model
Behavior
Generation
Sensors Structure Actuators
World
AV Examples
Phoenix Mars Lander
Artist: Corby Waste, JPL
AV Examples
Stanford/Volkswagen’s Stanley:
2005 DARPA Grand Challenge
AV Examples
CMU: TARTAN Racing - Boss
2007 DARPA Urban Challenge
“… to autonomously
navigate in town and in traffic. Boss uses
perception, planning and behavioral software
to reason about traffic and take appropriate actions
while proceeding safely to a destination. “
IAV: Control Impact
Value
Judgment
Planning
Sensor
World
Processing Model
Behavior
Generation
Behavior Coordination
World
Model
Behavior
Generation
Sensor
Processing
Sensors Structure Actuators
Sensors
Structure
World
World
Actuators
Enabling Technological Advances
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•
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Computational Hardware
Sensors and Sensor Processing
Computational Reasoning
Control Theoretic Advances
Software Engineering Principles
Planning
Behavior Coordination
World
Model
Behavior
Generation
Sensor
Processing
Sensors
This talk:
Structure
Actuators
World
One perspective on how such advances enable advancing AV capability
Topics:
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Deliberative & reactive planning
Behaviors & nonlinear control
Discrete event & hybrid systems
Theory & practicality: Cognitive mapping
Computational Reasoning: AI
“The science and engineering of making intelligent machines” – John McCarthy, 1956
Benchmark Intelligent (Human) Capabilities:
• Deduction, reasoning, problem solving
• Natural language understanding
• Knowledge representation
• Planning & scheduling
• Learning
Intelligence: the ability of a system to act
appropriately in an uncertain environment, where
• Vision
appropriate action is that which increased the
probability of success, and success is the
• ……
achievement of behavioral subgoals that support the
system’s ultimate goal.
-J. S. Albus
Computational Reasoning: Planning
O
N
C
C
A
T
N
O
T
N
O
A
C
Initial State
A
N
T
O
Final State
Discovery of an action sequence to achieve a goal
• Formulation: Initial state, final state, action set, cost
• Implementation: Search (e.g. A*), hierarchical tasks, heuristics
• Challenges: Dealing w/ real world
– Dimensionality
– Model error
– Lack of determinism
AI Early Successes
Games, Theorem proving, Planning, etc.
Planning
R. Brooks (1987) questions status:
“Replication of human intelligence in a machine”
– Achieve success on AI component
• abstraction
• symbolic processing w/ simple semantics
• no uncertainty
World
Model
Behavior Coordination
Behavior
Generation
Sensor
Processing
Sensors
Structure
World
Actuators
Neglected Hard Issues:
• Recognition
• Spatial understanding
• Uncertainty & Noise
• Model error
• …..
Traditional “Mobile Robot Control”
Traditional approach:
-- Decompose human intelligence into (right) subpieces,
-- Progress on each subpiece,
-- Define (right) interfaces between subpieces
-- Reassemble subpieces
Sensors
Actuators
Criticism: Insufficient experience and knowledge to …
--R. Brooks (1987)
Behavior Based Control
• Capabilities of Intelligent Systems
– Built incrementally via task-achieving behaviors
– Complete functional systems at each step:
• to ensure pieces are valid
• to ensure interfaces are valid
Reason about Object Behaviors
Plan changes to World
Bn, ..... , B2, B1
Identify Objects
Sensors
Actuators
Monitor Changes
Build Maps
Behavior
Generation
Sensor
Processing
Explore
Sensors
Structure
Avoid Objects
World
Actuators
Planning & Reaction
Hierarchical Planning: Formulates
action sequence for long range goals
– Deliberation
• Time consuming
• Model based
– Adaptability for general tasks
Reactivity: Activates automatically
to ensure vehicle safety
– Direct reflexive perceptionaction links
• Tradeoff: optimal for safe
– Well-tested for fixed tasks
Many opportunities for control theoretic contributions:
• Behaviors provide interface
Discrete
Event
• Finite alphabet of discrete actions/events for planning
Systems
• Continuous desired trajectories to controllers
• Behaviors included control, but were not control theoretic
Nonlinear control
• Higher performance/robustness
...FL(a,b)FC(b,c)...
...FL(a,b)OA(a,b)FC(b,c)...
• Behavior switching requires
analysis
• Domains of attractions, controlled invariant sets
Hybrid systems
• Switching stability
a
b
b
• Adaptability
requires stableaperformance feedback
Adaptation & learning
• Environmental models
c
c
• Behavior
models: closed loop performance
• Etc.
Behavior based ‘control’ design via DES
Specify the set of events S, set of behaviors Q, and transition function d to
solve a given problem.
S – set of switching events
e(t)
Q – set of behaviors
i(t)
d – behavioral switching logic in response to events
i(t+)=d(e(t),i(t))
The resulting automaton can be represented as a graph.
• Discrete Event Controller, d(e(t),i(t))
DEC
– Switches among behaviors
•
Interface:
Generator
Event Trajectory
– Event generator
– Library of behaviors:
Q = {Bi}, i = 1,…N
• Trajectory generator
• Controller
World
Model
Sensor
Processing
Sensors
Behavior
Controller
Structure
World
Actuators
DES: Chemical Plume Tracing
Design behaviors Q, event definitions S, and transition function d such that
an autonomous underwater vehicle (AUV) will
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Proceed from a home location to a region of operation
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Search for a chemical plume
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Track a chemicalnn plume in a turbulent flow to its source
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Declare the source location
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Return home
Q: Behaviors
GT – go to point P
US – uninformed search
IS – informed search
MI – maintain: in
MO – maintain: out
PD – post-declaration maneuvers
S: Events
f – finish
c – detect chemical @td
n1 – no detection at t = td + t1
d – declare source
f
US
f
IS
GT(P)
c
f
c
c
MI
n1
f
MO
d
f
GT(H)
PD
DES: Chemical Plume Tracing
DES formulation provides systematic design/analysis structure
• Graph representation of d facilitates definition of specifications
within design team and with customer
• Behaviors Q
• Each behavipor designed to execute a specific trajectory
• Behavior/Control interface at the speed/heading command level
• New behaviors easily added
• Design: Q, S, d
• Biological emulation: moths, mosquitoes, salmon, …
• Understanding of vehicle kinematics, fluid flow, physics
• Informed search using HMM for chemical transport
• For CPT, stochastic DES sufficiently complex to preclude analytic analysis
• Analysis and design based on simulation
• At-sea surf-zone performance demonstration (3x)
CPT In-water Experimental Results (June 2003)
Mission 003
- OpArea is dashed line
- Trajectory in red
- Chemical detections in blue
What is a Behavior/Schema?
• A pattern of action as well as a pattern for action (Neisser 1976).
• A mental codification of experience that includes a particular
organized way of perceiving cognitively and responding to a
complex situation or set of stimuli (Merriam-Webster 1984).
• A control system that continually monitors … the system it controls
to determine the appropriate pattern of action for achieving the
motor schema’s goals (Overton 1984).
Arkin 1989
Behavior implementation requires control
• traditionally at the speed and yaw command level
• Speed & yaw control implementation is part of the hardware
• Alternative interfaces/behaviors may be desirable
• control is critical
• performance
• robustness
• different behaviors may necessitate different controllers
• switching between different controllers for different behaviors must be
performed in a stable manner
Behaviors: Simple
GoToPoint
GoToPoint
GoToPointYaw
GoToPointYaw
R, d
d
R,
R
R
Fluid Flow
Vector
FollowCurve
Search
FollowLine
p3
R p
1
p2
FL(p1),GP(p2),FL(p3),...
p0
p3
p1
p2
PY(p0),FL(p1),PY(p2),...
Behavior Examples: Land Vehicle
1. Throttle and wheel angle control
2. Speed (cruise) control
• Adaptive cruise control – slows to avoid collisions
3.
4.
5.
6.
Speed and yaw rate control
Speed and yaw angle control
Path following
Trajectory following
• Still may use speed and yaw as intermediate control variables
• Provides provably stable system
• Robustness analysis is possible
• Domain of attraction can be determined
7. Autonomous Parallel Parking of a Nonholonomic Vehicle
8. ... Avoid obstacle, follow target, change lane, exit, …
9. …Platoon: merge, exit, …
Behavior Examples: VSTOL
MODES
• CTOL – Conventional Takeoff & Landing
• VTOL – Vertical Takeoff & Landing
• Transition
CTOL
TRNSTN
VTOL
Key Ideas
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Stability via approximate feedback linearization
Maximal controlled invariant subset
Least restrictive feedback control
Flight envelope protection
Behavior Examples: Helicopter
• Behaviors: Motion primitives
– trim points, transition between trim points
• Tactical planning by hybrid automata:
– Selection of optimal sequence of motion primitives:
• Vehicle state constraints
• Cost function
• Strategic objectives
– Each node of the automata is an agent (controller)
responsible for behavior implementation
Behaviors: Nonlinear Control
Operator
Input
OA ... OA Bn
DES Rn
Environmental
Mapping
Envir.
Sensor Proc.
NAV
FDI
FDI
Hierarchical
Navigation
Basic
Nav.
& Behav.
Planner
Planner
.... B4
B4B3
B3B2
B2 B1
B1
pc ,
c c
Control: B
pi
vc,c
Control: 
vcv,,
c cc
Control: v, 
Sensors Vehicle Actuators
Environment
u
Behavior based controller
• Library of behaviors:
{Bi}, i = 1,…N
– Each behavior: Bi
ai, bi, Wi are Class K functions
Hybrid/Switched Systems
V1
V2
V1
V2
V1
....
Issues
• No Zeno:
t1,1 t2,1 t1,2 t2,2 t1,3
Guaranteed via trajectory generator portion of
planner/behavior
t2,3
• Behavior stability:
Guaranteed via nonlinear control design/analysis given
that behavior i starts with xi2i
• Switching stability:
– Requires
AUV for Hull Search
Behaviors:
• velocity & angular rate
• velocity & attitude
• trajectory following w/ zero attitude
• trajectory following w/ nonzero attitude
• surface following
• hold position and attitude
• scan object at offset
Sim
Comments:
1.
Simulation is an essential tool
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2.
Implementation and test
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–
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3.
4.
idea evaluation
debugging
of complete systems
on real vehicles
in the real world
Contests:
• DARPA: Grand & Urban
• AUVSI: UAS, UGV, USV, AUV
• NIST: Search & Rescue
• SAUC-E
is the only real test of efficacy
Rigorous theoretical study: foundation to enable &
direct advancement in autonomous vehicle capabilities
Ingenuity: to address the practical complexities
beyond our theoretical understanding
Cognitive Mapping
• Egocentric: self-centered frame
– Object locations change as the vehicle moves
– Uses: sensor information
• Allocentric: external reference frame
– Object locations are (largely) fixed
– Uses: planning, long-term memory
Human Example:
Home map (allocentric) facilitates planning
Vision (egocentric sensor) facilites maneuvering
Simultaneous Localization and Mapping
Setting: Initiate an AV at an unknown location in an
unknown environment:
1. Develop a map M of the unknown environment
2. Maintain knowledge of the AV position Pv w/i the
unknown environment
Assuming only egocentric sensing D
landmark info:
di – distance and bi – bearing
dead-reckoning:
odometry or inertial
No anchoring (i.e., sensors such as GPS are not used)
SLAM Theoretical solution w/ properties in 2001
• Stochastic & Kalman filter methods
• Linear assumptions
Practical SLAM: Challenges
1. System: noise, nonlinearity, observability issues
2. Dimensionality:
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Number of variables
•
•
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3.
4.
5.
6.
Position variables: 3*(#landmarks+1)
Covariance matrix: 9*(#landmarks+1)2
Topography: grid or triangular tessalation
Topology
Correspondence or Data Association: Ego to Allo issues
Time variation: object motion, aging, changing topology
Exploration: optimization w/ map uncertainty
Sensor fusion: combining heterogeneous information
from various sensor modalities
Similar Complex IAV Problems
• Cognitive Mapping
• Perception
– Sensor Fusion/Feature Correspondence
• Behavioral Learning
• Optimal Control
– Approximate dynamic programming
• Mission Planning
– Heuristics, hierarchies, …
Concluding Comments
• Turing Test:
–
–
–
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Optimal
Strong super-human: performs better than all humans
Super human: performs better than most humans
Sub-human: performs worse than most humans
• Intelligent AV Capabilities, e.g.:
Navigation
Control
Data fusion
Map building
Plan management
Learning
– All involve feedback processes, w/ many challenging & unsolved
problems
– Control expertise has & continues to expand its role, both
developing & utilizing new tools, to yield increasingly robust and
capable systems
• The concept of behaviors, combined w/ advanced control methods,
enables robust abstraction for higher level IAV performance
Caption:
It locates and destroys mines at the
command of an operator who’s
nowhere in the vicinity
Thank you
Agile AV SW/HW Development
Tenets
• Simplicity
– Start w/ simplest approach
– Always have a functioning
prototype
– Add functionality as needed
• Feedback &
Communications
– From customer
– From team
• Behavior specification
• Unit test specification
• Simulation test
– From system
• Freq. vehicle testing
Operator
Input
DES Rn
OA ... OA Bn
Environmental
Mapping
Envir.
Sensor Proc.
NAV
FDI
FDI
Hierarchical
Navigation
Basic
Nav.
Planner
& Behav.
Planner
.... B4
B4B3
B3B2
B2 B1
B1
pc ,
c c
Control: B
pi
vc,c
Control: 
vcv,,
c cc
Control: v, 
Sensors Vehicle Actuators
Environment
u
Intelligent AV: Implementation
“Optimism is an occupational hazard of programming,
feedback is the treatment.”
Kent Beck
Test Failure Scenarios Software Engineering
• Hardware (HW)
• Software (SW) Compile
• SW Link
• HW/SW Mismatch: drivers
• SW Logic
• SW Parameters
• Sensor/SW/event/mission
Unconsidered Scenarios
• Agile Programming
• Version Control
• Object Oriented Programming
– Reuseable & maintainable SW
– Standard behavior interface:
init, model, control,
reference trajectory generator,
event alphabet
• DES & FSM Tools