Emergent Task Allocation for Mobile Robots

1. Emergent Task Allocation for Mobile Robots Nuzhet Atay Doctoral Student Seminar Advisor : Burchan Bayazit

2. Motivation • Given an unknown environment and a swarm of mobile robots • Achieve some goals under a set of constraints • Explore the environment • Regions of interest • Dynamic • Unpredictable • Spread or shrink • Obstacles • Real-life scenarios Nuzhet Atay

3. Robotic Systems • Heterogeneous robots with limited • Speed • Sensing range • Communication range • Multiple robot coordination • Task allocation • Goal: • Optimum assignment of robots Nuzhet Atay

4. Planning and Task Allocation Task Distribution Task Distribution • Multi-robot systems require efficient and accurate planning • Global optimal solutions are expensive • communication overhead • planning time • Our Solution: An emergent approach • Emergent: Solution results from interactions of robots • Local approximation to global optimal • Low cost and feasible in real-time Nuzhet Atay

5. Outline • Problem Definition • Model • Centralized (Global Optimal) Solution • Emergent Approach • Comparison of two methods • Experimental Results • Conclusion Nuzhet Atay

6. Problem Definition • Objective is to assign robots to • Cover regions of interest • Provide communication between all robots • Control maximum total surface • Explore new regions • We can define this problem as an optimization problem • Given the robot information and environment properties • What is each robot’s ideal next step? Nuzhet Atay

7. Model • Robots • Constant communication and sensing range • Limited speed • Regions of interest • Targets that need to be tracked by the robots • Several robots may be needed • Input: • Information about the robots and the environment • Expected target positions after n steps • Output • Optimum locations of robots Nuzhet Atay

8. Centralized Solution Task Assignment Information Collection • Problem is defined as a mixed-integer linear program • Non-linear constraints • Flexible • Easy to customize • Objective: maximize • Target Coverage • Communication • Area Coverage • Exploration R7 T3 R8 R9 R6 T1 R4 R5 R3 T2 R1 R2 Central Server Task Allocation is Determined Nuzhet Atay

9. Target Coverage • Each robot has a sensing range • Each target has a coverage requirement • A target is covered • Necessary number of robots has the target in sensing range Nuzhet Atay

10. Communication • Two robots can communicate • If they are within communication range of each other • There is a series of robots that can provide communication Nuzhet Atay

11. Area Coverage • Maximum area coverage is obtained • Sensor overlap is minimized Nuzhet Atay

12. Exploration • Robots store the places they have visited • Each robot tries to locate itself outside the explored region Nuzhet Atay

13. Optimum Distribution • Sample distribution for maximizing • Target coverage • Communication • Area coverage Nuzhet Atay

14. Additional Constraints • Environment obstacles • Convex • Partitioned into convex obstacles • Convex box surrounding the obstacle Nuzhet Atay

15. Problems of Global Approach • Solution is not feasible with large number of robots • Solving mixed-integer linear program is NP-Hard • Central server • Too much data transfer • Our solution: • Solve small local problems • Integrate to approximate optimal solution • Advantage is to avoid • Communication overhead • Exponential computation time Nuzhet Atay

16. Emergent Task Allocation Find a Solution with Local Information Information Sharing • Robots find optimal solutions with local information • Each robot has different information about • Robots in the environment • Targets to be tracked • Environment properties • Solutions are different • Independent suboptimal solutions • To find better solution, robots • Exchange information • Recompute their solutions • Final result depends on • Information content • Number of iterations T3 R7 R8 R9 R6 T1 R4 R5 R3 T2 R1 R2 Recompute Solution with Updated Information Nuzhet Atay

17. Intentions • Robots send their intended locations to neighbors • Each robot assumes these locations are final • Finds its optimal location Nuzhet Atay

18. Directives • Robots send expected locations of neighbors • Each robot chooses the best among them Nuzhet Atay

19. Intentions and Directives • Robots send both their and neighbors computed locations • Each robot finds the best location using options Nuzhet Atay

20. Intentions, Directives and Target • Robots send • Their and their neighbors’s locations • Possible target assignments • Each robot • Decides a target assignment • Finds the best location using options Nuzhet Atay

21. Comparison of Global and Emergent • Emergent approach is more efficient • Computation • Communication • Approximate solution to global optimal Nuzhet Atay

22. Experiments • How well emergent performs? • Comparison to global • Experiment scenario • 8 robots • 6 targets • 3 obstacles • How scalable is the emergent? • 20 robots – 10 targets • 30 robots – 15 targets Nuzhet Atay

23. Solution - Global Nuzhet Atay

24. Solution - Emergent Nuzhet Atay

25. Comparison • Solution quality is comparable # of Targets Covered at Each Step Nuzhet Atay

26. Evaluation • Emergent approach is 400 times faster than global approach Solution Time Nuzhet Atay

27. Scalability • Execution time remains constant with larger networks Nuzhet Atay

28. Conclusion • Planning framework for multi-robot task allocation • Low communication cost and suitable for real-time applications • 400 times faster than the global optimal solution • Comparable solution • Future work: • Different negotiation strategies • Implementation on real robots • Different utility functions Nuzhet Atay

29. Questions Motion Planning Group http://www.cse.wustl.edu/~bayazit ? Nuzhet Atay

30. Convergence • ETA approaches to CGO after finite number of steps • Observation: • If all robots find the same solution, then this solution is the same as CGO • At each step • Robots find a solution • Exchange information and negotiate • Assuming all state information is shared • Robots will have information about other robots’ views • After p steps • All robots have the same information and find the same solution Nuzhet Atay

31. Solution Quality Nuzhet Atay

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