English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/167160
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE
Exportar a otros formatos:


Searching and tracking people with cooperative mobile robots

AuthorsGoldhoorn, Alex ; Garrell, Anaís ; Alquézar Mancho, Renato ; Sanfeliu, Alberto
KeywordsUrban robotics
Multi-robot coordination
Decentralized coordination
Issue Date2018
PublisherSpringer Nature
CitationAutonomous Robots 42(4): 739-759 (2018)
AbstractSocial robots should be able to search and track people in order to help them. In this paper we present two different techniques for coordinated multi-robot teams for searching and tracking people. A probability map (belief) of a target person location is maintained, and to initialize and update it, two methods were implemented and tested: one based on a reinforcement learning algorithm and the other based on a particle filter. The person is tracked if visible, otherwise an exploration is done by making a balance, for each candidate location, between the belief, the distance, and whether close locations are explored by other robots of the team. The validation of the approach was accomplished throughout an extensive set of simulations using up to five agents and a large amount of dynamic obstacles; furthermore, over three hours of real-life experiments with two robots searching and tracking were recorded and analysed.
Publisher version (URL)https://doi.org/10.1007/s10514-017-9681-6
Identifiersdoi: 10.1007/s10514-017-9681-6
e-issn: 1573-7527
issn: 0929-5593
Appears in Collections:(IRII) Artículos
Files in This Item:
File Description SizeFormat 
searchtracking.pdf690,76 kBAdobe PDFThumbnail
Show full item record
Review this work

WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.