Planning with ants Efficient path planning with rapidly exploring random trees and ant colony optimization
Rapidly exploring random trees (RRTs) have been proven to be efficient for planning in environments populated with obstacles. These methods perform a uniform sampling of the state space, which is needed to guarantee the algorithm’s completeness but does not necessarily lead to the most efficient sol...
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| Veröffentlicht in: | International journal of advanced robotic systems Jg. 13; H. 5 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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SAGE Publications
07.10.2016
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| Abstract | Rapidly exploring random trees (RRTs) have been proven to be efficient for planning in environments populated with obstacles. These methods perform a uniform sampling of the state space, which is needed to guarantee the algorithm’s completeness but does not necessarily lead to the most efficient solution. In previous works it has been shown that the use of heuristics to modify the sampling strategy could incur an improvement in the algorithm performance. However, these heuristics only apply to solve the shortest path-planning problem. Here we propose a framework that allows us to incorporate arbitrary heuristics to modify the sampling strategy according to the user requirements. This framework is based on ‘learning from experience’. Specifically, we introduce a utility function that takes the contribution of the samples to the tree construction into account; sampling at locations of increased utility then becomes more frequent. The idea is realized by introducing an ant colony optimization concept in the RRT/RRT* algorithm and defining a novel utility function that permits trading off exploitation versus exploration of the state space. We also extend the algorithm to allow an anytime implementation. The scheme is validated with three scenarios: one populated with multiple rectangular obstacles, one consisting of a single narrow passage and a maze-like environment. We evaluate its performance in terms of the cost and time to find the first path, and in terms of the evolution of the path quality with the number of iterations. It is shown that the proposed algorithm greatly outperforms state-of-the-art RRT and RRT* algorithms. |
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| AbstractList | Rapidly exploring random trees (RRTs) have been proven to be efficient for planning in environments populated with obstacles. These methods perform a uniform sampling of the state space, which is needed to guarantee the algorithm’s completeness but does not necessarily lead to the most efficient solution. In previous works it has been shown that the use of heuristics to modify the sampling strategy could incur an improvement in the algorithm performance. However, these heuristics only apply to solve the shortest path-planning problem. Here we propose a framework that allows us to incorporate arbitrary heuristics to modify the sampling strategy according to the user requirements. This framework is based on ‘learning from experience’. Specifically, we introduce a utility function that takes the contribution of the samples to the tree construction into account; sampling at locations of increased utility then becomes more frequent. The idea is realized by introducing an ant colony optimization concept in the RRT/RRT* algorithm and defining a novel utility function that permits trading off exploitation versus exploration of the state space. We also extend the algorithm to allow an anytime implementation. The scheme is validated with three scenarios: one populated with multiple rectangular obstacles, one consisting of a single narrow passage and a maze-like environment. We evaluate its performance in terms of the cost and time to find the first path, and in terms of the evolution of the path quality with the number of iterations. It is shown that the proposed algorithm greatly outperforms state-of-the-art RRT and RRT* algorithms. |
| Author | Losada, Rafael Ortiz Viseras, Alberto Merino, Luis |
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| Cites_doi | 10.1177/0278364914547786 10.1109/3477.484436 10.1177/0278364911406761 10.1163/156855300741960 10.1109/IROS.2003.1248805 10.1177/02783640122067453 10.1177/0278364912444543 10.1109/ROBOT.2000.844730 10.1016/j.ejor.2006.06.046 10.1109/TRO.2010.2049527 10.1109/TRO.2014.2340191 10.1177/0278364915577958 10.1089/10665270252935395 10.1109/70.508439 10.1007/10991541_25 |
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| Issue | 5 |
| Keywords | rapidly exploring random trees ant colony optimization Mobile robots autonomous agents motion and path planning bio-inspired robotics |
| Language | English |
| License | This article is distributed under the terms of the Creative Commons Attribution 3.0 License (http://www.creativecommons.org/licenses/by/3.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
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| References | Siméon, Laumond, Nissoux 2000; 14 Janson, Schmerling, Clark 2015; 34 Rickert, Sieverling, Brock 2014; 30 Gammell, Srinivasa, Barfoot 2014 Persson, Sharf 2014; 33 Socha, Dorigo 2008; 185 Kavraki, Svestka, Latombe 1996; 12 Nasir, Islam, Malik 2013 Dorigo, Maniezzo, Colorni 1996; 26 Jaillet, Cortés, Siméon 2010; 26 Kobilarov 2012; 31 LaValle, Kuffner 2001; 20 Karaman, Frazzoli 2011; 30 Amato, Song 2002; 9 bibr32-1729881416664078 Salzman O (bibr11-1729881416664078) Hsu D (bibr18-1729881416664078); 3 bibr2-1729881416664078 Akgun B (bibr27-1729881416664078) bibr23-1729881416664078 Guibas LJ (bibr17-1729881416664078); 1 Jaillet L (bibr20-1729881416664078) bibr19-1729881416664078 bibr14-1729881416664078 bibr8-1729881416664078 bibr1-1729881416664078 Arslan O (bibr5-1729881416664078) bibr22-1729881416664078 Diankov R (bibr31-1729881416664078) Nasir J (bibr25-1729881416664078) 2013 Mohamad MM (bibr33-1729881416664078) Alterovitz R (bibr28-1729881416664078) Denny J (bibr12-1729881416664078) bibr26-1729881416664078 Arslan O (bibr6-1729881416664078) bibr13-1729881416664078 Denny J (bibr15-1729881416664078) bibr4-1729881416664078 Ferguson D (bibr35-1729881416664078) Kim D (bibr24-1729881416664078) bibr34-1729881416664078 Karaman S (bibr9-1729881416664078) bibr30-1729881416664078 bibr3-1729881416664078 Gammell JD (bibr7-1729881416664078) 2014 bibr16-1729881416664078 Burns B (bibr21-1729881416664078) bibr29-1729881416664078 Salzman O (bibr10-1729881416664078) |
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| Snippet | Rapidly exploring random trees (RRTs) have been proven to be efficient for planning in environments populated with obstacles. These methods perform a uniform... |
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| Subtitle | Efficient path planning with rapidly exploring random trees and ant colony optimization |
| Title | Planning with ants |
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