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

IWPSS 2006 - Brennan Sellner bsellner@andrew.cmu.edu Slide 2

Motivation

•Human workers:• Predict likely outcomes• Move between teamsmid-task

•Can multi-robotteams do thesame?

IWPSS 2006 - Brennan Sellner bsellner@andrew.cmu.edu Slide 3

What is Proactive Replanning?

• Predict problems and opportunities

•Replan before they manifest

IWPSS 2006 - Brennan Sellner bsellner@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

IWPSS 2006 - Brennan Sellner bsellner@andrew.cmu.edu Slide 5

Results Preview

• Stochastic domain•Metric is schedule makespan•Makespan reductions of 11-32%

IWPSS 2006 - Brennan Sellner bsellner@andrew.cmu.edu Slide 6

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

IWPSS 2006 - Brennan Sellner bsellner@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

IWPSS 2006 - Brennan Sellner bsellner@andrew.cmu.edu Slide 10

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

IWPSS 2006 - Brennan Sellner bsellner@andrew.cmu.edu Slide 13

Duration Prediction: Why?

IWPSS 2006 - Brennan Sellner bsellner@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

IWPSS 2006 - Brennan Sellner bsellner@andrew.cmu.edu Slide 15

Live Task Modification: Why?

IWPSS 2006 - Brennan Sellner bsellner@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

IWPSS 2006 - Brennan Sellner bsellner@andrew.cmu.edu Slide 17

Live Task Modification: How?

•Challenge: search large space of teams and agents

IWPSS 2006 - Brennan Sellner bsellner@andrew.cmu.edu Slide 18

Experimental Results

• Scenario•Conditions•Data

IWPSS 2006 - Brennan Sellner bsellner@andrew.cmu.edu Slide 19

Experimental Scenario

IWPSS 2006 - Brennan Sellner bsellner@andrew.cmu.edu Slide 20

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

IWPSS 2006 - Brennan Sellner bsellner@andrew.cmu.edu Slide 24

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)

IWPSS 2006 - Brennan Sellner bsellner@andrew.cmu.edu Slide 25

Future Work

• Function approximation and Duration Prediction•Durative agent transfers•Risk management

IWPSS 2006 - Brennan Sellner bsellner@andrew.cmu.edu Slide 26

Summary

• Initial implementation of Proactive Replanning•Results are promising:

• Makespan reductions of up to 32%• Further work is underway

IWPSS 2006 - Brennan Sellner bsellner@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.