Biased Sampling Potentially Guided Intelligent Bidirectional RRT∗ Algorithm for UAV Path Planning in 3D Environment
During the last decade, Rapidly-exploring Random Tree star (RRT∗) algorithm based on sampling has been widely used in the field of unmanned aerial vehicle (UAV) path planning for its probabilistically complete and asymptotically optimal characteristics. However, the convergence rate of RRT∗ as well...
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2019/5157403 |
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doaj-5caae17e4a5e4883a8edf5abeeea6e6a2020-11-25T02:04:08ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472019-01-01201910.1155/2019/51574035157403Biased Sampling Potentially Guided Intelligent Bidirectional RRT∗ Algorithm for UAV Path Planning in 3D EnvironmentXiaojing Wu0Lei Xu1Ran Zhen2Xueli Wu3School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, ChinaSchool of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, ChinaSchool of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, ChinaSchool of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, ChinaDuring the last decade, Rapidly-exploring Random Tree star (RRT∗) algorithm based on sampling has been widely used in the field of unmanned aerial vehicle (UAV) path planning for its probabilistically complete and asymptotically optimal characteristics. However, the convergence rate of RRT∗ as well as B-RRT∗ and IB-RRT∗ is slow for these algorithms perform pure exploration. To overcome the weaknesses above, Biased Sampling Potentially Guided Intelligent Bidirectional RRT∗ (BPIB-RRT∗) algorithm is proposed in this paper, which combines the bidirectional artificial potential field method with the idea of bidirectional biased sampling. The proposed algorithm flexibly adjusts the sampling space, greatly reduces the invalid spatial sampling, and improves the convergence rate. Moreover, the deeply theoretical analysis of the proposed BPIB-RRT∗ algorithm is given regarding its probabilistic completeness, asymptotic optimality, and computational complexity. Finally, compared to the latest UAV path planning algorithms, simulation comparisons are demonstrated to show the superiority of our proposed BPIB-RRT∗ algorithm.http://dx.doi.org/10.1155/2019/5157403 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiaojing Wu Lei Xu Ran Zhen Xueli Wu |
spellingShingle |
Xiaojing Wu Lei Xu Ran Zhen Xueli Wu Biased Sampling Potentially Guided Intelligent Bidirectional RRT∗ Algorithm for UAV Path Planning in 3D Environment Mathematical Problems in Engineering |
author_facet |
Xiaojing Wu Lei Xu Ran Zhen Xueli Wu |
author_sort |
Xiaojing Wu |
title |
Biased Sampling Potentially Guided Intelligent Bidirectional RRT∗ Algorithm for UAV Path Planning in 3D Environment |
title_short |
Biased Sampling Potentially Guided Intelligent Bidirectional RRT∗ Algorithm for UAV Path Planning in 3D Environment |
title_full |
Biased Sampling Potentially Guided Intelligent Bidirectional RRT∗ Algorithm for UAV Path Planning in 3D Environment |
title_fullStr |
Biased Sampling Potentially Guided Intelligent Bidirectional RRT∗ Algorithm for UAV Path Planning in 3D Environment |
title_full_unstemmed |
Biased Sampling Potentially Guided Intelligent Bidirectional RRT∗ Algorithm for UAV Path Planning in 3D Environment |
title_sort |
biased sampling potentially guided intelligent bidirectional rrt∗ algorithm for uav path planning in 3d environment |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2019-01-01 |
description |
During the last decade, Rapidly-exploring Random Tree star (RRT∗) algorithm based on sampling has been widely used in the field of unmanned aerial vehicle (UAV) path planning for its probabilistically complete and asymptotically optimal characteristics. However, the convergence rate of RRT∗ as well as B-RRT∗ and IB-RRT∗ is slow for these algorithms perform pure exploration. To overcome the weaknesses above, Biased Sampling Potentially Guided Intelligent Bidirectional RRT∗ (BPIB-RRT∗) algorithm is proposed in this paper, which combines the bidirectional artificial potential field method with the idea of bidirectional biased sampling. The proposed algorithm flexibly adjusts the sampling space, greatly reduces the invalid spatial sampling, and improves the convergence rate. Moreover, the deeply theoretical analysis of the proposed BPIB-RRT∗ algorithm is given regarding its probabilistic completeness, asymptotic optimality, and computational complexity. Finally, compared to the latest UAV path planning algorithms, simulation comparisons are demonstrated to show the superiority of our proposed BPIB-RRT∗ algorithm. |
url |
http://dx.doi.org/10.1155/2019/5157403 |
work_keys_str_mv |
AT xiaojingwu biasedsamplingpotentiallyguidedintelligentbidirectionalrrtalgorithmforuavpathplanningin3denvironment AT leixu biasedsamplingpotentiallyguidedintelligentbidirectionalrrtalgorithmforuavpathplanningin3denvironment AT ranzhen biasedsamplingpotentiallyguidedintelligentbidirectionalrrtalgorithmforuavpathplanningin3denvironment AT xueliwu biasedsamplingpotentiallyguidedintelligentbidirectionalrrtalgorithmforuavpathplanningin3denvironment |
_version_ |
1724944388422893568 |