dynamic mission planning for multiple mobile robots

Dynamic Mission Planning for Multiple Mobile Robots

Barry Brumitt and Anthony Stentz26 Oct, 1999AMRS-99 Class PresentationBrian Chemel


Overview

Problem descriptionMission grammarSystem architecture: GRAMMPSBrief digression: D*ResultsAnalysis and limitations


Problem Description: Environment

Dynamic, complex environment Typical situation

World state initially unknown Runtime observations incorporated into

shared world model


Problem Description: Robots

Multiple, heterogeneous mobile robots Assumptions

Robots have relatively open workspaces, so solution set is not too sparse

Effective positioning, communication and perception are a given


Problem Description: Goals

Robot task: move to specified locations, in specified order Reconnaissance example

Waypoints to be scouted by team of robots

Warehouse example Multiple pickup points, to be followed by

multiple delivery points


Problem Description: GRAMMPS Planner

Military reconnaissance task Outdoor environment Multiple robot vehicles (Navlab

HMMWV’s, in practice) Multiple distributed goals, with

sequencing Requires mission grammar to pass

parameters to distributed planning system


Mission Grammar

Expression Meaning“Do A, then do B”

A OR B

A AND B

Robot i

Goal j

“Move robot r to goal g”

BA

BA BA iR

jG

),( grM


Mission Grammar: Example

“Move either robot 1 or robot 2 to goals 1, 2, 3, and 4. Then move both robots 1 and 2 to goal 5.”

),(),( 521432121 GRRMGGGGRRM


System Architecture: Overview

Global (shared) dynamic plannersGlobal (shared) mission plannerLocal (individual) plan execution

Mission Planner

D* D* D* D* D*…

Dynamic Planners (one per goal)

Robot 1 Robot 2 Robot n…


System Architecture: Local Navigators

Input Path to assigned

goal Perception

information

Output Steering

commands

Mission Planner

D* D* D* D* D*…




System Architecture: Mission Planner

Input Estimated path

costs for each (robot,goal) pair

Output Mapping from

robots to goals

Algorithm TSP heuristic

Mission Planner

D* D* D* D* D*…




System Architecture:Dynamic Planners

Input Current world

state knowledge

Output Path to each goal

for each robot

Planning algorithm D*

Mission Planner

D* D* D* D* D*…




Brief Digression: The D* Algorithm

Modification of the A* planning algorithmProvides efficient, optimal and complete path planning in unknown, partially known, and changing environmentsAs new information about the environment is learned, cost information is propagated through state spaceEach time new information makes previous path calculations obsolete, a new path is calculatedOriginal paper: Stentz, ICRA ‘94


Example: Simulation Initial Plan

Mission statement:

),(),( 521432121 GRRMGGGGRRM


Example: Simulation Final Plan

Goals re-assigned on the flyMission successfully completed


Highlights

Demonstration implementation on Navlab HMMWVs allows real-time, team-based mission planning in dynamic environmentsSystem scales gracefully up to large numbers of robots and goals


Limitations

Waypoint task structure is very limitingNo discussion of how to modify TSP approach to allow heterogeneityOne robot must act as “leader” of the entire teamWhen D* fails, system can lock up


Related Work

Stentz, T. “Optimal and Efficient Path Planning for Partially-Known Environments.” ICRA ’94Brumitt, B., Stentz, T. “GRAMMPS: A Generalized Mission Planner for Multiple Mobile Robots in Unstructured Environments.” ICRA ‘98Brumitt, B., Hebert, M. “Experiments in Autonomous Driving With Concurrent Goals and Multiple Vehicles.” ICRA ‘98

dynamic mission planning for multiple mobile robots

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