Multirobot Coordination in USAR Katia Sycara The Robotics Institute

2 Motivation Rate of change of environment is slow No model of uncertainty due to uniqueness of disaster Incident Commander provides initial map and goals (do ASAP)

3 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)

4 Problem Formalization Time Critical Tight Coordination Team Planning Problem: R : Robots G : Goals E : Environment C : System constraints T max : Time allocated for mission R : Robots G : Goals Goal rewards decrease with time Maximize reward subject to constraints

5 Mathematical Programming Optimize objective function Linear Program (LP): Maximize c T x (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) Q 1 f(x 1 )+…Q m f(x m ) Q i : reward for goal i x i : time that goal i is done f(x i ): dependence on time Goal requirements must be met Robots must take legal paths System constraints must be met Some variables must be integer (½ Robot???)

6 Experimental Results Fractured Subteams Dynamic Replanning How much better is the anytime algorithm? Number of goals Anytime algorithm: Combination of MILP And heuristic Koes, Nourbakhsh, Sycara, “Heterogeneous, Multi-robot coordination with Spatial and temporal constraints, AAAI-05, Pittsburgh, PA. July 2005.

7 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)

8 Results of SDM with Communication Failures Performance compared to perfect communication Partitions in Environment 1 of 5 robots was disabled at randomly selected time 10 possible goals

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10 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

11 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?

12 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

13 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)