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

15 Citations (Scopus)


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
Number of pages15
ISBN (Print)9783030238063
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
ISSN (Print)0302-9743


ConferenceThe 20th Towards Autonomous Robotic Systems Conference (TAROS 2019)
Abbreviated titleTAROS 2019
Country/TerritoryUnited Kingdom


  • probabilistic planning
  • ROSPlan
  • robotics
  • task planning


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