Towards Proactive Replanningfor Multi-Robot Teams
Brennan Sellner and Reid Simmons 5th International Workshop on Planning and Scheduling for SpaceOctober 23, 2006 Trestle
ProjectRobotics Institute
Carnegie Mellon University
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Motivation
•Human workers:• Predict likely outcomes• Move between teamsmid-task
•Can multi-robotteams do thesame?
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What is Proactive Replanning?
• Predict problems and opportunities
•Replan before they manifest
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Idea
• Iterative repair planner•Add Proactive Replanning
• Duration Prediction• Live Task Modification
•Replan and modify active teams to:
• Forestall problems• Grasp opportunities
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Results Preview
• Stochastic domain•Metric is schedule makespan•Makespan reductions of 11-32%
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Approach Overview
•Domain•Architecture•Duration Prediction• Live Task Modification
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Domain
•Multi-agent, multi-team assembly•Goal: Minimize schedule length
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Scenario
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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
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Planner
•ASPEN: Iterative repair and optimization•Duration Prediction within constraint network• Live Task Modification during repair and optimization
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Planner: Conflict Resolution
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Planner: Optimization
•Metric: schedule length•Use idle agents to:
• Start tasks on the “critical path”• Speed up executing tasks on the critical path
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Duration Prediction: Why?
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Duration Prediction: How?
• Predict remaining duration at each timestep•Replan in response•Challenge:
• Accurate predictions within resource bounds
•Current approach:• Offline simulation + lookup table
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Live Task Modification: Why?
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Live Task Modification: How?
•As part of schedule repair or optimization
• Heuristically select a new team• Subject to constraints• Currently assume instant transfers
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Live Task Modification: How?
•Challenge: search large space of teams and agents
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Experimental Results
• Scenario•Conditions•Data
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Experimental Scenario
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Experimental Approach
• 50 simulated assemblies per condition• 4 conditions
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Baseline Condition
•ASPEN•No Proactive Replanning• Each time step:
• Right-shift• Left-shift• Optimize• Repair
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Experimental Conditions
•Baseline, plus:• Prediction, or:• Live Modification, or:• Combined
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Summary of Results
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Results Details
50 runsper conditionmean (std dev)
Baseline(ASPEN)
Prediction Modification Combination
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)
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Future Work
• Function approximation and Duration Prediction•Durative agent transfers•Risk management
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Summary
• Initial implementation of Proactive Replanning•Results are promising:
• Makespan reductions of up to 32%• Further work is underway
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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.