Vehicle Autonomy and Intelligent Control

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Vehicle Autonomy and Intelligent Control

• J. A. Farrell
• Department of Electrical Engineering
• University of California, Riverside

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Intelligence Autonomy
Increasingly capable autonomous vehicles a
worthy challenge necessitating increased ability
along various dimensions of intelligence
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AV Examples
Phoenix Mars Lander Artist Corby Waste, JPL
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AV Examples
Stanford/Volkswagens Stanley 2005 DARPA
Grand Challenge
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AV Examples
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IAV Control Impact
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Enabling Technological Advances

• Computational Hardware
• Sensors and Sensor Processing
• Computational Reasoning
• Control Theoretic Advances
• Software Engineering Principles
• This talk
• One perspective on how such advances enable
  advancing AV capability
• Topics
• Deliberative reactive planning
• Behaviors nonlinear control
• Discrete event hybrid systems
• Theory practicality Cognitive mapping

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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
• Vision

Intelligence the ability of a system to act
appropriately in an uncertain environment, where
appropriate action is that which increased the
probability of success, and success is the
achievement of behavioral subgoals that support
the systems ultimate goal. -J. S. Albus
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Computational Reasoning Planning

• 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

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AI Early Successes

• Games, Theorem proving, Planning, etc.
• 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

• Neglected Hard Issues
• Recognition
• Spatial understanding
• Uncertainty Noise
• Model error
• ..

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Traditional Mobile Robot Control
Traditional approach -- Decompose human
intelligence into (right) subpieces, --
Progress on each subpiece, -- Define (right)
interfaces between subpieces -- Reassemble
subpieces Criticism Insufficient
experience and knowledge to --R. Brooks (1987)
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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

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Planning Reaction

• Reactivity Activates automatically to ensure
  vehicle safety
• Direct reflexive perception-action links
• Tradeoff optimal for safe
• Well-tested for fixed tasks

• Hierarchical Planning Formulates action sequence
  for long range goals
• Deliberation
• Time consuming
• Model based
• Adaptability for general tasks

• Many opportunities for control theoretic
  contributions
• Behaviors provide interface
• Finite alphabet of discrete actions/events for
  planning
• Continuous desired trajectories to controllers
• Behaviors included control, but were not control
  theoretic
• Higher performance/robustness
• Behavior switching requires analysis
• Domains of attractions, controlled invariant
  sets
• Switching stability
• Adaptability requires stable performance
  feedback
• Environmental models
• Behavior models closed loop performance
• Etc.

Discrete Event Systems Nonlinear
control Hybrid systems Adaptation learning
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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)?(e(t),i(t))
• The resulting automaton can be represented as a
  graph.
• Discrete Event Controller, ?(e(t),i(t))
• Switches among behaviors
• Interface
• Event generator
• Library of behaviors Q Bi, i 1,N
• Trajectory generator
• Controller

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DES Chemical Plume Tracing

• Design behaviors Q, event definitions S, and
  transition function ? such that
• an autonomous underwater vehicle (AUV) will
• Proceed from a home location to a region of
  operation
• Search for a chemical plume
• Track a chemicalnn plume in a turbulent flow to
  its source
• Declare the source location
• Return home

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DES Chemical Plume Tracing

• DES formulation provides systematic
  design/analysis structure
• Graph representation of ? 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)

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CPT In-water Experimental Results (June 2003)

• Mission 003
• OpArea is dashed line
• Trajectory in red
• Chemical detections in blue

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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
  schemas 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

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Behaviors Simple
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Behavior Examples Land Vehicle

• Throttle and wheel angle control
• Speed (cruise) control
• Adaptive cruise control slows to avoid
  collisions
• 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
• Autonomous Parallel Parking of a Nonholonomic
  Vehicle
• ... Avoid obstacle, follow target, change lane,
  exit,
• Platoon merge, exit,

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Behavior Examples VSTOL

• MODES
• CTOL Conventional Takeoff Landing
• VTOL Vertical Takeoff Landing
• Transition
• Key Ideas
• Stability via approximate feedback linearization
• Maximal controlled invariant subset
• Least restrictive feedback control
• Flight envelope protection

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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

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Behaviors Nonlinear Control
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Behavior based controller

• Library of behaviors Bi, i 1,N
• Each behavior Bi

ai, bi, Wi are Class K functions
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Hybrid/Switched Systems

• Issues
• No Zeno Guaranteed via trajectory generator
  portion of planner/behavior
• Behavior stability Guaranteed via nonlinear
  control design/analysis given that behavior i
  starts with xi2?i
• Switching stability
• Requires

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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
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Comments

• Simulation is an essential tool
• idea evaluation
• debugging
• Implementation and test
• of complete systems
• on real vehicles
• in the real world
• 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

• Contests
• DARPA Grand Urban
• AUVSI UAS, UGV, USV, AUV
• NIST Search Rescue
• SAUC-E

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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

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Simultaneous Localization and Mapping

• Setting Initiate an AV at an unknown location in
  an unknown environment
• Develop a map M of the unknown environment
• 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

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Practical SLAM Challenges

• System noise, nonlinearity, observability issues
• Dimensionality
• Number of variables
• Position variables 3(landmarks1)
• Covariance matrix 9(landmarks1)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

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Similar Complex IAV Problems

• Cognitive Mapping
• Perception
• Sensor Fusion/Feature Correspondence
• Behavioral Learning
• Optimal Control
• Approximate dynamic programming
• Mission Planning
• Heuristics, hierarchies,

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Concluding Comments

• Turing Test
• 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.
• 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

Navigation Control
Data fusion Map building
Plan management Learning
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Thank you
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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

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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