Probabilistic Planning for Robotics with ROSPlan

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

19 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 publicationTowards Autonomous Robotic Systems - 20th Annual Conference, TAROS 2019, Proceedings
EditorsKaspar Althoefer, Jelizaveta Konstantinova, Ketao Zhang
PublisherSpringer Verlag
Pages236-250
Number of pages15
ISBN (Print)9783030238063
DOIs
Publication statusPublished - 2019
Event20th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2019 - London, United Kingdom
Duration: 3 Jul 20195 Jul 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11649 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2019
Country/TerritoryUnited Kingdom
CityLondon
Period3/07/20195/07/2019

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