Vehicle Autonomy and Intelligent Control - PowerPoint PPT Presentation


PPT – Vehicle Autonomy and Intelligent Control PowerPoint presentation | free to view - id: 214ca0-ZDc1Z


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation

Vehicle Autonomy and Intelligent Control


Adaptability requires stable performance feedback. Environmental models ... Strong super-human: performs better than all humans. Super human: performs better ... – PowerPoint PPT presentation

Number of Views:106
Avg rating:3.0/5.0
Slides: 36
Provided by: eeU3


Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: Vehicle Autonomy and Intelligent Control

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
AV Examples
Phoenix Mars Lander Artist Corby Waste, JPL
AV Examples
Stanford/Volkswagens Stanley 2005 DARPA
Grand Challenge
AV Examples
IAV Control Impact
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

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
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

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
  • ..

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)
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

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

Discrete Event Systems Nonlinear
control Hybrid systems Adaptation learning
Behavior based control design via DES
  • Specify the set of events S, set of behaviors Q,
    and transition function d to solve a given
  • 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
  • Discrete Event Controller, ?(e(t),i(t))
  • Switches among behaviors
  • Interface
  • Event generator
  • Library of behaviors Q Bi, i 1,N
  • Trajectory generator
  • Controller

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
  • Search for a chemical plume
  • Track a chemicalnn plume in a turbulent flow to
    its source
  • Declare the source location
  • Return home

DES Chemical Plume Tracing
  • DES formulation provides systematic
    design/analysis structure
  • Graph representation of ? facilitates definition
    of specifications within design team and with
  • Behaviors Q
  • Each behavipor designed to execute a specific
  • 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,
  • 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
    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
  • Alternative interfaces/behaviors may be desirable
  • control is critical
  • performance
  • robustness
  • different behaviors may necessitate different
  • switching between different controllers for
    different behaviors must be performed in a stable

Behaviors Simple
Behavior Examples Land Vehicle
  • Throttle and wheel angle control
  • Speed (cruise) control
  • Adaptive cruise control slows to avoid
  • 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
  • ... Avoid obstacle, follow target, change lane,
  • Platoon merge, exit,

Behavior Examples VSTOL
  • 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

Behavior Examples Helicopter
  • Behaviors Motion primitives
  • trim points, transition between trim points
  • Tactical planning by hybrid automata
  • Selection of optimal sequence of motion
  • Vehicle state constraints
  • Cost function
  • Strategic objectives
  • Each node of the automata is an agent
    (controller) responsible for behavior

Behaviors Nonlinear Control
Behavior based controller
  • Library of behaviors Bi, i 1,N
  • Each behavior Bi

ai, bi, Wi are Class K functions
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

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

  • 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
  • Ingenuity to address the practical complexities
    beyond our theoretical understanding
  • Contests
  • DARPA Grand Urban
  • NIST Search Rescue
  • SAUC-E

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
  • 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
  • SLAM Theoretical solution w/ properties in 2001
  • Stochastic Kalman filter methods
  • Linear assumptions

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
  • Time variation object motion, aging, changing
  • 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
  • Optimal
  • Strong super-human performs better than all
  • 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

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

Intelligent AV Implementation
  • Optimism is an occupational hazard of
    programming, feedback is the treatment. Kent

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