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Cooperative Control of Distributed Autonomous Vehicles in Adversarial Environments

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Title: Cooperative Control of Distributed Autonomous Vehicles in Adversarial Environments


1
Cooperative Control of Distributed Autonomous
Vehicles in Adversarial Environments
  • AFOSR 2001 MURI Kickoff
  • Caltech/Cornell/MIT/UCLA
  • May 14, 2001

2
Agenda
3
Vision Networks of (semi) Autonomous Vehicles
  • Large scale operations
  • Fault tolerance through redundant deployment
  • Cost effectiveness through simple/specialized
    components
  • Complex collective behavior through simple local
    behavior

4
Challenges
  • Local information/decision making
  • Constrained communications
  • Large scale of operations
  • Uncertain dynamic environment
  • Hostile adversarial presence

5
Approach
  • Multidisciplinary
  • Multiscale Modeling Hierarchical Planning
    Logical Programming Environments Complexity
    Management Distributed Protocols Language
    Adaptation Biological Modeling
  • Analytical Constructive
  • Experimental
  • Case Study Simulations Hybrid Hardware
    Realization

6
Hierarchical Formulation
  • Fundamental challenge lack of centralized
    decisions
  • Analytical difficulties
  • Computational complexity
  • Meaningful tractable?
  • Relevance of hierarchy
  • Aggregation, distributed computation, reduced
    communication, etc.
  • Supporting social/biological evidence
  • Self similarity

7
Research Focus
  • Scalability, modeling reduction
  • Representation of distributed low level
    components in a manner amenable to high level
    planning with reduced complexity.
  • High level planning
  • Development of analytical methods and
    computational algorithms for coordinated team
    strategies.
  • Low level planning
  • Realization of team strategies through low level
    strategies and optimization.
  • Communications
  • Investigation of communications issues within
    and among levels.

8
Case Studies
  • Motivate Illustrate Research
  • Multi-vehicle tasking with obstacle and mutual
    avoidance (one-sided)
  • Autonomous suppression of enemy defenses
    (two-sided)

9
Experimental Testbeds
  • Two hybrid facilities
  • HiFi virtual vehicles with hardware
    communications
  • Simple hardware vehicles with virtual
    communications/distribution
  • Internal open-platform investigations

10
Expected Outcomes
  • Theory Analytical understanding of achievable
    performance of distributed cooperative control
    systems.
  • Computation Algorithms software tools for
    control design, testing, evaluation, and rapid
    prototyping.
  • Experimentation Application to simulated and
    hardware testbeds.
  • Education Multidisciplinary program with
    increased DoD visibility.

11
Expected Insights
  • How to address scalability through modeling
    decomposition.
  • How to address computational complexity in
    hierarchical designs.
  • How to develop reliable multi-layered cooperative
    strategies.
  • How to counter adversarial actions with
    constrained communications.
  • How to integrate local optimizations for
    collective performance.
  • How to synchronize cooperating elements through
    modeling and ID.
  • How to exploit neurological models to design
    cooperating elements.
  • How to achieve reliable communications in
    hierarchical structures.
  • How to derive adaptive languages for autonomous
    operations.

12
Team Strengths People
Caltech Jason Hickey (CS) Richard Murray (CDS/ME) Cornell Raffaello DAndrea (MAE) Bart Selman (CS) Carla Gomes (CS)
MIT Munther Dahleh (EE) Eric Feron (AE) Steve Massaquoi (EE/Neuro) Brian Williams (AE) UCLA David Chichka (AE) Greg Pottie (EE) Jeff Shamma (MAE) Jason Speyer (MAE) Charles Taylor (Bio)
13
Team Strengths Experience
Caltech DARPA SEC DURIP Cornell RoboCup AFRL/Cornell Intelligent Information Systems Institute
MIT DARPA JFACC NSF Natural motor control ONR Distributed cooperative languages UCLA DARPA JFACC ONR Minuteman NSF Learning in natural artificial systems
14
Agenda
15
Project Management
  • Monthly seminar series
  • Twice annual research meetings
  • Graduate student exchanges

16
Technical Approach
  • Scalability, Modeling Reduction
  • Representation of distributed low level
    components in a manner amenable to high level
    planning with reduced complexity.
  • High level planning
  • Low level planning
  • Communications

17
Scalability, Modeling Reduction
  • Objective Reduction of complexity for design.
  • Example Coordinated flight
  • Each vehicle represents control problem
  • Want real time coordinated flight planning
  • Answer Quantization?

18
Quantization
  • Naïve suggestion Discretize state space?
  • Issue Reduction not related to control problem.
  • Alternative
  • Presume individual aircraft controllers.
  • Discretize via primitives of achievable
    maneuvers.

19
Robust Hybrid Automata
  • Recent work at MIT for single vehicle
  • Automata Nodes (state subset) connected by
    trajectories
  • Hybrid Trajectories executed by dynamical system
  • Robust Tolerance set for node transitions
  • Reduction directly related to control problem.

20
Quantization Consequences
  • Complexity reduction.
  • Low level limitations captured in admissible high
    level node transitions.
  • Node transitions can be human inspired.
  • Can define primitive strings to continue
    hierarchy.
  • Discrete state !

21
Higher Order Primitives
  • Simple example String of primitives
  • Need to derive coordinated primatives
  • Will involve multiple RHA
  • Needed for complementary vehicle packages, e.g.,
    maneuverability vs vulnerability
  • Draw upon existing expertise
  • Construct play sequences
  • Control interpretation Feedforward

22
Encoding of Primitives
  • Objective Capture high level intent
  • Previous example Node sequences/Play sequences
  • Alternative Parameters in the low level
    optimization

23
Encoding via Objective Functions
  • Recent work at Caltech for real-time trajectory
    generation
  • uto,toT arg min ? L(x,u) dt F(x(T),u(T))
  • dx/dt f(x,u), g(x,u) lt 0
  • Exploit structure of dynamics and warm restart
    to solve real-time.

24
Encoded Objectives, cont
  • Can use penalty functions as encoded objectives
  • Example Cooperative trajectory planning
  • Node sequence terminal penalty sequence
  • Objective function mutual avoidance
  • Different penalty functions reflect different
    cooperative roles, e.g., lead vs middle vs trailer

25
Encoded Objectives, cont
  • Recent work at MIT (DARPA/JFACC) for hierarchical
    encoding

Reduced order model AND Reduced order objective
26
Uncertain Adversarial Conditions
  • Uncertain environment motivated robust hybrid
    automaton
  • Hostile adversary implies non-deterministic
    outcomes
  • Approach non-deterministic robust hybrid
    automata
  • Examples
  • Random outcome of battle engagement
  • Set-valued enemy actions

27
Model Reduction with Adversaries
  • Deterministic case Hierarchical consistency
  • Random case Averaged consistency (e.g.,
    manufacturing systems)
  • Adversarial case Cannot average
  • Approach Encoding of low level adversarial
    encounters

28
Adversarial Encoding, cont
  • Recent work at UCLA (DARPA/JFACC) for high-level
    adversarial modeling
  • Actual engagment Sector-by-sector
  • Model engagement Single sector
  • Allowed game-theoretic constructions

29
Technical Approach
  • Scalability, Modeling Reduction
  • High level planning
  • Development of analytical methods and
    computational algorithms for coordinated team
    strategies.
  • Low level planning
  • Communications

30
High Level Planning
  • Hierarchical reduction likely leads to
    quantization.
  • Increasing relevance of CS
  • Combinatorial complexity management
  • Logical programming environment
  • Control question Find coarseness formulations
    that allow analytical insight.

31
High Level Planning, cont
  • Conceptual approach is top-down or 2-way
  • Opted for designed behavior vs emergent
    behavior
  • Motivated by desire to meet specifications
    understand performance trade-offs.

32
High Level Complexity Management
  • Dimensionality reduction via higher order
    primitives
  • Recent work at MIT with randomized algorithms
  • Random waypoint selection in configuration space
  • Valuable in real-time obstacle avoidance
  • Two-time scale complexity management
  • Slow scale reinforcement learning/NDP
  • Fast scale randomized algorithms

33
Fast/Slow Scale Issues
  • Computational obstacles persist despite
    dimensionality reduction
  • Recent work at Cornell for complexity
  • Critical problem formulation
  • Algorithm portfolios

34
Phase Transitions and Randomization
  • Identify critical variable/constraint ratio
  • Abrupt transition of practical complexity
  • Understanding phase transition guides problem
    formulations
  • Can approach sub-critical problem envelope
  • Can bring in randomized algorithm portfolio

35
Logical Programming Environments
  • Design questions become logical issues in
    discrete high level models
  • Utility of logical programming environment
  • Automated checking of design
  • Assistance in exploratory design
  • Verifiable re-use of existing design

36
LPE, cont
  • High level planning can decompose into
  • Scheduling tasks
  • Executing tasks
  • Example
  • Formation
  • Avoidance
  • Regroup
  • Abort

37
LPE, cont
  • Design question Under what scenarios will
    vehicle collection successfully avoid obstacle?
  • Formulate as logical question based on subtask
    and subgoals.
  • Leads to multi-layered questions (within high
    level).
  • Will benefit from complexity management research.

38
Team Strategies in Adversarial Environments
  • Main team issue Available information
  • Major analytical difficulties with simple
    deviation from centralized info
  • Example Linear decentralized control
  • Limited results for one two sided team problems

39
Team Strategies, cont
  • Approach Exploit quantization computation
  • Impose coarseness until analytical solution
    structure emerges
  • Use new insight to guide heuristic designs
  • Example UCLA DARPA/JFACC efforts

40
Low Level Planning
  • Scalability, Modeling Reduction
  • High level planning
  • Low level planning
  • Realization of team strategies through low level
    strategies and optimization.
  • Communications

41
Local Optimization vs Global Objectives
  • Low level optimization need not be consistent
    with high level intent
  • Subtle issue Same team becomes competitors
  • Examples Stadium viewing, collective feeding,
    voting mechanisms, prisoners dilemma

42
Conscientious Local Optimization
  • Recent work at UCLA considers self-restraining
    local optimization
  • Form model of interactions with team
  • Optimize based on interactive model
  • Self-impose conforming to model
  • Leads to handshaking of model achievable
    performance
  • Interactive models both stochastic deterministic

43
Uncertain Adversarial Conditions
  • Traditional receding horizon dilemma
  • Short horizon for uncertainty (poor prediction)
  • Long horizon for stability
  • Aggravated by existence of adversary
  • Approach Encode uncertainty/adversary in penalty
    functions

44
Cooperative Identification
  • Local execution requires synchronized mode of
    operation
  • Example High order primitive of trajectory
    sequence
  • Approach Recent work at MIT (DARPA/JFACC) on
    intent ID applied to team members
  • Communications viewpoint Constrained consensus

45
Natural Cooperative Architectures
  • Recent work at MIT on control architecture models
    of low level brain features
  • Direct How higher order primitives are
    assembled, recalled, executed.
  • Indirect How internal models are resolved for
    prediction of future environmental interactions.

46
Communications
  • What vs how to communicate?
  • Priorities
  • Quantity
  • QoS requirements
  • Approach Combine distributed mobile networks
    with controls communication requirements for new
    reliable protocols.

47
Adaptive Languages
  • Controls/Communication intersection
  • Pre-programmed vehicles with local information
  • Collective behavior determined by communicated
    information
  • Control decision communications decision
  • Approach Adaptive languages/protocols as new
    perspective in highly autonomous operations

48
Agenda
  • Scalability, modeling reduction
  • Representation of distributed low level
    components in a manner amenable to high level
    planning with reduced complexity.
  • High level planning
  • Development of analytical methods and
    computational algorithms for coordinated team
    strategies.
  • Low level planning
  • Realization of team strategies through low level
    strategies and optimization.
  • Communications
  • Investigation of communications issues within
    and among levels.

49
Agenda
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