Probabilistic planning for robotics with ROSPlan

Gerard Canal, Michael Cashmore, Senka Krivic, Guillem Alenyà, Daniele Magazzeni, Carme Torras

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

23 Citations (Scopus)

Abstract

Probabilistic planning is very useful for handling uncertainty in planning tasks to be carried out by robots. ROSPlan is a framework for task planning in the Robot Operating System (ROS), but until now it has not been possible to use probabilistic planners within the framework. This systems paper presents a standardized integration of probabilistic planners into ROSPlan that allows for reasoning with non-deterministic effects and is agnostic to the probabilistic planner used. We instantiate the framework in a system for the case of a mobile robot performing tasks indoors, where probabilistic plans are generated and executed by the PROST planner. We evaluate the effectiveness of the proposed approach in a real-world robotic scenario.
Original languageEnglish
Title of host publicationAnnual Conference Towards Autonomous Robotic Systems Conference (TAROS)
EditorsKaspar Althoefer, Jelizaveta Konstantinova, Ketao Zhang
Place of PublicationCham
PublisherSpringer
Pages236-250
Number of pages15
Volume11649
ISBN (Print)9783030238063
DOIs
Publication statusPublished - 28 Jun 2019
EventThe 20th Towards Autonomous Robotic Systems Conference (TAROS 2019) - Queen Mary University of London, London, United Kingdom
Duration: 3 Jul 20195 Jul 2019

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume11649
ISSN (Print)0302-9743

Conference

ConferenceThe 20th Towards Autonomous Robotic Systems Conference (TAROS 2019)
Abbreviated titleTAROS 2019
Country/TerritoryUnited Kingdom
CityLondon
Period3/07/195/07/19

Keywords

  • probabilistic planning
  • ROSPlan
  • robotics
  • task planning

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