Multirobot Coordination in USAR

Katia SycaraThe Robotics Institute

katia@cs.cmu.eduwww.cs.cmu.edu/~softagents

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MotivationRate of

change of environment

is slow

No model of uncertainty due to uniqueness of disaster

Incident Commander provides initial map and goals (do ASAP)

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Technical Challenges

• Joint goals require robots to work together• Heterogeneous multirobot task allocation• Robots must coordinate schedules• Additional system constraints• Communication failures• Need good allocation/schedule since

traveling is slow• Team must quickly react to discrepancies

in plan (interleaving planning & execution)

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Problem FormalizationTime Critical Tight Coordination Team Planning Problem:• R: Robots • G: Goals• E: Environment• C: System

constraints• Tmax: Time

allocated for mission

R: RobotsG: Goals

Goal rewards decrease with time

Maximize reward subject to constraints

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Mathematical Programming• Optimize objective function

• Linear Program (LP): Maximize cTx (x is vector of variables)Subject to Ax b (constraints)l x u (bounds)

• Mixed Integer Linear Program (MILP): some variables must be integer (much harder)

Q1f(x1)+…Qmf(xm)Qi: reward for goal ixi: time that goal i is donef(xi): dependence on time

Goal requirements must be metRobots must take legal paths

System constraints must be met

Some variables must be integer(½ Robot???)

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Experimental Results

• Fractured Subteams• Dynamic Replanning

How much better is the anytime algorithm?

Number of goals

Anytime algorithm: Combination of MILPAnd heuristic

Koes, Nourbakhsh, Sycara, “Heterogeneous, Multi-robot coordination with Spatial and temporal constraints, AAAI-05, Pittsburgh, PA. July 2005.

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Replanning with Fractured Subteams

Challenge: The “optimal” plan when replanning may fail since robots in other

fractured subteams follow initial plan

Our approach: Avoid changes to the schedule that affect robots in other

fractured subteams (especially short term)

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Results of SDM with Communication Failures

00.10.20.30.40.50.60.70.80.9

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2 5 10

No replanning Full replanning SDM

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Partitions in Environment

1 of 5 robots was disabled at randomly selected time 10 possible goals

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Contributions

• General framework for coordination with multirobot task allocation, scheduling, system constraints

• Time critical tight coordination team planning problem (Multirobot/Time Extended)– Formalization– Benchmarks– Analysis

• System constraints: design & implementation• Fractured subteams model• Selective Disruption Minimization• Robust plan generation

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Challenges of Large Scale Teams• Coordinate large number of UAVs in dynamic, open and

hostile environments– Limited communication channels– Local information– High uncertainty, high dynamics, complex environment– High “failure” rate– Impractical to centralize

• Maintaining team status model– Prohibitive volume of communication– Can we limit what needs to be known by others?

• New problems when knowledge is localized

• Existing approaches only work for small teams (10s)– Require accurate models of team activities or a centralized

information broker• Key coordination algorithms are typically NP-Complete

(or worse)– Can we build scalable, generic algorithms?

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Results

Developed scalable algorithms for distributed and autonomous:

• Plan instantiation • Role allocation• Information sharing• Resource allocation• Sensor fusion• Recovering from faulty sensor readings• Tested in team of hundreds of agents

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Challenges

Urban Search and Rescue

• Uncertainty• Time pressure• Difficult environment• Heterogeneous robots

• Spatial and temporal constraints

• Communication failures• Human-robot interactions

Unstructured (after explosion)

Undamaged(chemical leak)