Heuristic for vehicle motion planning with search trees
Motion planning is an essential part of an autonomous system. It finds a sequence of actions for the vehicle to move from start to goal. In this thesis, we study the use of heuristic in the context of motion planning. We propose a heuristic method using a pre-computed heuristic look up table for sam...
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ndltd-UPSALLA1-oai-DiVA.org-uu-4321852021-01-19T05:33:59ZHeuristic for vehicle motion planning with search treesengGuo, PeiliUppsala universitet, Institutionen för informationsteknologi2020Engineering and TechnologyTeknik och teknologierMotion planning is an essential part of an autonomous system. It finds a sequence of actions for the vehicle to move from start to goal. In this thesis, we study the use of heuristic in the context of motion planning. We propose a heuristic method using a pre-computed heuristic look up table for sampling based motion planning method Rapidly-exploring random trees (RRT). The proposed method is successfully implemented and evaluated in nine different scenarios. We have found improvement in the quality of the path at a short time out in at least six out of the nine scenarios. In certain scenarios, we are able to observe up to 20% reduction of execution time and up to 90% of improvement of the path quality. Student thesisinfo:eu-repo/semantics/bachelorThesistexthttp://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-432185IT ; 20092application/pdfinfo:eu-repo/semantics/openAccess |
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English |
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Others
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Engineering and Technology Teknik och teknologier |
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Engineering and Technology Teknik och teknologier Guo, Peili Heuristic for vehicle motion planning with search trees |
description |
Motion planning is an essential part of an autonomous system. It finds a sequence of actions for the vehicle to move from start to goal. In this thesis, we study the use of heuristic in the context of motion planning. We propose a heuristic method using a pre-computed heuristic look up table for sampling based motion planning method Rapidly-exploring random trees (RRT). The proposed method is successfully implemented and evaluated in nine different scenarios. We have found improvement in the quality of the path at a short time out in at least six out of the nine scenarios. In certain scenarios, we are able to observe up to 20% reduction of execution time and up to 90% of improvement of the path quality. |
author |
Guo, Peili |
author_facet |
Guo, Peili |
author_sort |
Guo, Peili |
title |
Heuristic for vehicle motion planning with search trees |
title_short |
Heuristic for vehicle motion planning with search trees |
title_full |
Heuristic for vehicle motion planning with search trees |
title_fullStr |
Heuristic for vehicle motion planning with search trees |
title_full_unstemmed |
Heuristic for vehicle motion planning with search trees |
title_sort |
heuristic for vehicle motion planning with search trees |
publisher |
Uppsala universitet, Institutionen för informationsteknologi |
publishDate |
2020 |
url |
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-432185 |
work_keys_str_mv |
AT guopeili heuristicforvehiclemotionplanningwithsearchtrees |
_version_ |
1719373507406594048 |