1. Towards Proactive Replanning for Multi-Robot Teams Brennan Sellner and Reid Simmons 5th International Workshop on Planning and Scheduling for Space October 23, 2006 Trestle Project Robotics Institute Carnegie Mellon University

2. IWPSS 2006 - Brennan Sellnerbsellner@andrew.cmu.edu Slide 2 Motivation Human workers: Predict likely outcomes Move between teams mid-task Can multi-robot teams do the same?

3. IWPSS 2006 - Brennan Sellnerbsellner@andrew.cmu.edu Slide 3 What is Proactive Replanning? Predict problems and opportunities Replan before they manifest

4. IWPSS 2006 - Brennan Sellnerbsellner@andrew.cmu.edu Slide 4 Idea Iterative repair planner Add Proactive Replanning Duration Prediction Live Task Modification Replan and modify active teams to: Forestall problems Grasp opportunities

5. IWPSS 2006 - Brennan Sellnerbsellner@andrew.cmu.edu Slide 5 Results Preview Stochastic domain Metric is schedule makespan Makespan reductions of 11-32%

6. IWPSS 2006 - Brennan Sellnerbsellner@andrew.cmu.edu Slide 6 Approach Overview Domain Architecture Duration Prediction Live Task Modification

7. IWPSS 2006 - Brennan Sellnerbsellner@andrew.cmu.edu Slide 7 Domain Multi-agent, multi-team assembly Goal: Minimize schedule length

8. IWPSS 2006 - Brennan Sellnerbsellner@andrew.cmu.edu Slide 8 Scenario

9. IWPSS 2006 - Brennan Sellnerbsellner@andrew.cmu.edu Slide 9 Architecture Planner (ASPEN): Centralized Repairs & optimizes schedule Dispatches tasks Duration Prediction & Live Task Modification Executive: Manages execution of tasks Monitors resource usage Transmits state to planner Behavioral: Interfaces with hardware Transmits state to executive Behavioral and hardware both simulated

10. IWPSS 2006 - Brennan Sellnerbsellner@andrew.cmu.edu Slide 10 Planner ASPEN: Iterative repair and optimization Duration Prediction within constraint network Live Task Modification during repair and optimization

11. IWPSS 2006 - Brennan Sellnerbsellner@andrew.cmu.edu Slide 11 Planner: Conflict Resolution

12. IWPSS 2006 - Brennan Sellnerbsellner@andrew.cmu.edu Slide 12 Planner: Optimization Metric: schedule length Use idle agents to: Start tasks on the “critical path” Speed up executing tasks on the critical path

13. IWPSS 2006 - Brennan Sellnerbsellner@andrew.cmu.edu Slide 13 Duration Prediction: Why?

14. IWPSS 2006 - Brennan Sellnerbsellner@andrew.cmu.edu Slide 14 Duration Prediction: How? Predict remaining duration at each timestep Replan in response Challenge: Accurate predictions within resource bounds Current approach: Offline simulation + lookup table

15. IWPSS 2006 - Brennan Sellnerbsellner@andrew.cmu.edu Slide 15 Live Task Modification: Why?

16. IWPSS 2006 - Brennan Sellnerbsellner@andrew.cmu.edu Slide 16 Live Task Modification: How? As part of schedule repair or optimization Heuristically select a new team Subject to constraints Currently assume instant transfers

17. IWPSS 2006 - Brennan Sellnerbsellner@andrew.cmu.edu Slide 17 Live Task Modification: How? Challenge: search large space of teams and agents

18. IWPSS 2006 - Brennan Sellnerbsellner@andrew.cmu.edu Slide 18 Experimental Results Scenario Conditions Data

19. IWPSS 2006 - Brennan Sellnerbsellner@andrew.cmu.edu Slide 19 Experimental Scenario

20. IWPSS 2006 - Brennan Sellnerbsellner@andrew.cmu.edu Slide 20 Experimental Approach 50 simulated assemblies per condition 4 conditions

21. IWPSS 2006 - Brennan Sellnerbsellner@andrew.cmu.edu Slide 21 Baseline Condition ASPEN No Proactive Replanning Each time step: Right-shift Left-shift Optimize Repair

22. IWPSS 2006 - Brennan Sellnerbsellner@andrew.cmu.edu Slide 22 Experimental Conditions Baseline, plus: Prediction, or: Live Modification, or: Combined

23. IWPSS 2006 - Brennan Sellnerbsellner@andrew.cmu.edu Slide 23 Summary of Results

24. IWPSS 2006 - Brennan Sellnerbsellner@andrew.cmu.edu Slide 24 Results Details 50 runs per condition mean (std dev) Baseline (ASPEN) PredictionModificationCombination Schedule length (s) 1176.90 (343.58) 1050.14 (273.32) 820.84 (123.55) 802.76 (141.53) Reduction in length ---- (----)10.8%30.3%31.8% Repair episodes 30.04 (36.60) 123.28 (23.06) 15.78 (8.00) 97.38 (23.86) Useful team modifications 35.26 (6.15) 35.14 (5.46) 51.64 (6.19) 48.58 (7.58)

25. IWPSS 2006 - Brennan Sellnerbsellner@andrew.cmu.edu Slide 25 Future Work Function approximation and Duration Prediction Durative agent transfers Risk management

26. IWPSS 2006 - Brennan Sellnerbsellner@andrew.cmu.edu Slide 26 Summary Initial implementation of Proactive Replanning Results are promising: Makespan reductions of up to 32% Further work is underway

27. IWPSS 2006 - Brennan Sellnerbsellner@andrew.cmu.edu Slide 27 Thanks! The executive's first name was Tanner, A shy, but proactive, replanner Who solved every trouble With a change, on the double, Which finished the job in fine manner.