Multi-agent informed path planning using the probability hypothesis density
An Informed Path Planning algorithm for multiple agents is presented. It can be used to efficiently utilize available agents when surveying large areas, when total coverage is unattainable. Internally the algorithm has a Probability Hypothesis Density (PHD) representation, inspired by modern multi-t...
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Linköpings universitet, Reglerteknik
2020
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Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-164628 https://doi.org/10.1007/s10514-020-09904-1 |
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ftlinkoepinguniv:oai:DiVA.org:liu-164628 2023-05-15T15:06:01+02:00 Multi-agent informed path planning using the probability hypothesis density Olofsson, Jonatan Hendeby, Gustaf Lauknes, Tom Rune Johansen, Tor Arne 2020 application/pdf http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-164628 https://doi.org/10.1007/s10514-020-09904-1 eng eng Linköpings universitet, Reglerteknik Linköpings universitet, Tekniska fakulteten Norwegian Univ Sci and Technol, Norway Norut, Norway SPRINGER Autonomous Robots, 0929-5593, 2020, 44, s. 913-925 orcid:0000-0002-1971-4295 http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-164628 doi:10.1007/s10514-020-09904-1 ISI:000516005000001 info:eu-repo/semantics/openAccess Path planning Target tracking Probability hypothesis density (PHD) Multi-agent Computer Sciences Datavetenskap (datalogi) Article in journal info:eu-repo/semantics/article text 2020 ftlinkoepinguniv https://doi.org/10.1007/s10514-020-09904-1 2022-05-01T08:25:11Z An Informed Path Planning algorithm for multiple agents is presented. It can be used to efficiently utilize available agents when surveying large areas, when total coverage is unattainable. Internally the algorithm has a Probability Hypothesis Density (PHD) representation, inspired by modern multi-target tracking methods, to represent unseen objects. Using the PHD, the expected number of observed objects is optimized. In a sequential manner, each agent maximizes the number of observed new targets, taking into account the probability of undetected objects due to previous agents actions and the probability of detection, which yields a scalable algorithm. Algorithm properties are evaluated in simulations, and shown to outperform a greedy base line method. The algorithm is also evaluated by applying it to a sea ice tracking problem, using two datasets collected in the Arctic, with reasonable results. An implementation is provided under an Open Source license. Funding Agencies|European Unions Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie GrantEuropean Union (EU) [642153]; Research Council of Norway through the Centres of Excellence funding scheme [223254 -NTNU-AMOS]; Center for Industrial Information Technology at Linkoping University (CENIIT); Fram Centre project Article in Journal/Newspaper Arctic Sea ice LIU - Linköping University: Publications (DiVA) Arctic Norway Autonomous Robots 44 6 913 925 |
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Open Polar |
collection |
LIU - Linköping University: Publications (DiVA) |
op_collection_id |
ftlinkoepinguniv |
language |
English |
topic |
Path planning Target tracking Probability hypothesis density (PHD) Multi-agent Computer Sciences Datavetenskap (datalogi) |
spellingShingle |
Path planning Target tracking Probability hypothesis density (PHD) Multi-agent Computer Sciences Datavetenskap (datalogi) Olofsson, Jonatan Hendeby, Gustaf Lauknes, Tom Rune Johansen, Tor Arne Multi-agent informed path planning using the probability hypothesis density |
topic_facet |
Path planning Target tracking Probability hypothesis density (PHD) Multi-agent Computer Sciences Datavetenskap (datalogi) |
description |
An Informed Path Planning algorithm for multiple agents is presented. It can be used to efficiently utilize available agents when surveying large areas, when total coverage is unattainable. Internally the algorithm has a Probability Hypothesis Density (PHD) representation, inspired by modern multi-target tracking methods, to represent unseen objects. Using the PHD, the expected number of observed objects is optimized. In a sequential manner, each agent maximizes the number of observed new targets, taking into account the probability of undetected objects due to previous agents actions and the probability of detection, which yields a scalable algorithm. Algorithm properties are evaluated in simulations, and shown to outperform a greedy base line method. The algorithm is also evaluated by applying it to a sea ice tracking problem, using two datasets collected in the Arctic, with reasonable results. An implementation is provided under an Open Source license. Funding Agencies|European Unions Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie GrantEuropean Union (EU) [642153]; Research Council of Norway through the Centres of Excellence funding scheme [223254 -NTNU-AMOS]; Center for Industrial Information Technology at Linkoping University (CENIIT); Fram Centre project |
format |
Article in Journal/Newspaper |
author |
Olofsson, Jonatan Hendeby, Gustaf Lauknes, Tom Rune Johansen, Tor Arne |
author_facet |
Olofsson, Jonatan Hendeby, Gustaf Lauknes, Tom Rune Johansen, Tor Arne |
author_sort |
Olofsson, Jonatan |
title |
Multi-agent informed path planning using the probability hypothesis density |
title_short |
Multi-agent informed path planning using the probability hypothesis density |
title_full |
Multi-agent informed path planning using the probability hypothesis density |
title_fullStr |
Multi-agent informed path planning using the probability hypothesis density |
title_full_unstemmed |
Multi-agent informed path planning using the probability hypothesis density |
title_sort |
multi-agent informed path planning using the probability hypothesis density |
publisher |
Linköpings universitet, Reglerteknik |
publishDate |
2020 |
url |
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-164628 https://doi.org/10.1007/s10514-020-09904-1 |
geographic |
Arctic Norway |
geographic_facet |
Arctic Norway |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_relation |
Autonomous Robots, 0929-5593, 2020, 44, s. 913-925 orcid:0000-0002-1971-4295 http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-164628 doi:10.1007/s10514-020-09904-1 ISI:000516005000001 |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.1007/s10514-020-09904-1 |
container_title |
Autonomous Robots |
container_volume |
44 |
container_issue |
6 |
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913 |
op_container_end_page |
925 |
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1766337692395634688 |