Title: Cooperative Control of Distributed Autonomous Vehicles in Adversarial Environments
1Cooperative Control of Distributed Autonomous
Vehicles in Adversarial Environments
- AFOSR 2001 MURI Kickoff
- Caltech/Cornell/MIT/UCLA
- May 14, 2001
2Agenda
3Vision 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
4Challenges
- Local information/decision making
- Constrained communications
- Large scale of operations
- Uncertain dynamic environment
- Hostile adversarial presence
5Approach
- 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
6Hierarchical 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
7Research 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.
8Case Studies
- Motivate Illustrate Research
- Multi-vehicle tasking with obstacle and mutual
avoidance (one-sided) - Autonomous suppression of enemy defenses
(two-sided)
9Experimental Testbeds
- Two hybrid facilities
- HiFi virtual vehicles with hardware
communications - Simple hardware vehicles with virtual
communications/distribution - Internal open-platform investigations
10Expected 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.
11Expected 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.
12Team 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)
13Team 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
14Agenda
15Project Management
- Monthly seminar series
- Twice annual research meetings
- Graduate student exchanges
16Technical 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
17Scalability, Modeling Reduction
- Objective Reduction of complexity for design.
- Example Coordinated flight
- Each vehicle represents control problem
- Want real time coordinated flight planning
- Answer Quantization?
18Quantization
- Naïve suggestion Discretize state space?
- Issue Reduction not related to control problem.
- Alternative
- Presume individual aircraft controllers.
- Discretize via primitives of achievable
maneuvers.
19Robust 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.
20Quantization 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 !
21Higher 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
22Encoding of Primitives
- Objective Capture high level intent
- Previous example Node sequences/Play sequences
- Alternative Parameters in the low level
optimization
23Encoding 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.
24Encoded 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
25Encoded Objectives, cont
- Recent work at MIT (DARPA/JFACC) for hierarchical
encoding
Reduced order model AND Reduced order objective
26Uncertain 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
27Model 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
28Adversarial 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
29Technical Approach
- Scalability, Modeling Reduction
- High level planning
- Development of analytical methods and
computational algorithms for coordinated team
strategies. - Low level planning
- Communications
30High 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.
31High 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.
32High 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
33Fast/Slow Scale Issues
- Computational obstacles persist despite
dimensionality reduction - Recent work at Cornell for complexity
- Critical problem formulation
- Algorithm portfolios
34Phase 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
35Logical 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
36LPE, cont
- High level planning can decompose into
- Scheduling tasks
- Executing tasks
- Example
- Formation
- Avoidance
- Regroup
- Abort
37LPE, 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.
38Team 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
39Team 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
40Low Level Planning
- Scalability, Modeling Reduction
- High level planning
- Low level planning
- Realization of team strategies through low level
strategies and optimization. - Communications
41Local 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
42Conscientious 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
43Uncertain 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
44Cooperative 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
45Natural 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.
46Communications
- What vs how to communicate?
- Priorities
- Quantity
- QoS requirements
- Approach Combine distributed mobile networks
with controls communication requirements for new
reliable protocols.
47Adaptive 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
48Agenda
- 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.
49Agenda