Rapidly-Exploring Adaptive Sampling Tree*: A Sample-Based Path-Planning Algorithm for Unmanned Marine Vehicles Information Gathering in Variable Ocean Environments
This research presents a novel sample-based path planning algorithm for adaptive sampling. The goal is to find a near-optimal path for unmanned marine vehicles (UMVs) that maximizes information gathering over a scientific interest area, while satisfying constraints on collision avoidance and pre-spe...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/9/2515 |
| _version_ | 1797569305549733888 |
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| author | Chengke Xiong Hexiong Zhou Di Lu Zheng Zeng Lian Lian Caoyang Yu |
| author_facet | Chengke Xiong Hexiong Zhou Di Lu Zheng Zeng Lian Lian Caoyang Yu |
| author_sort | Chengke Xiong |
| collection | DOAJ |
| description | This research presents a novel sample-based path planning algorithm for adaptive sampling. The goal is to find a near-optimal path for unmanned marine vehicles (UMVs) that maximizes information gathering over a scientific interest area, while satisfying constraints on collision avoidance and pre-specified mission time. The proposed rapidly-exploring adaptive sampling tree star (RAST*) algorithm combines inspirations from rapidly-exploring random tree star (RRT*) with a tournament selection method and informative heuristics to achieve efficient searching of informative data in continuous space. Results of numerical experiments and proof-of-concept field experiments demonstrate the effectiveness and superiority of the proposed RAST* over rapidly-exploring random sampling tree star (RRST*), rapidly-exploring adaptive sampling tree (RAST), and particle swarm optimization (PSO). |
| first_indexed | 2024-03-10T20:09:06Z |
| format | Article |
| id | doaj.art-03a4ce64050a4c8396b7daca3b7e14f2 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T20:09:06Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-03a4ce64050a4c8396b7daca3b7e14f22023-11-19T23:01:30ZengMDPI AGSensors1424-82202020-04-01209251510.3390/s20092515Rapidly-Exploring Adaptive Sampling Tree*: A Sample-Based Path-Planning Algorithm for Unmanned Marine Vehicles Information Gathering in Variable Ocean EnvironmentsChengke Xiong0Hexiong Zhou1Di Lu2Zheng Zeng3Lian Lian4Caoyang Yu5School of Oceanography, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Oceanography, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Oceanography, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Oceanography, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Oceanography, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Oceanography, Shanghai Jiao Tong University, Shanghai 200240, ChinaThis research presents a novel sample-based path planning algorithm for adaptive sampling. The goal is to find a near-optimal path for unmanned marine vehicles (UMVs) that maximizes information gathering over a scientific interest area, while satisfying constraints on collision avoidance and pre-specified mission time. The proposed rapidly-exploring adaptive sampling tree star (RAST*) algorithm combines inspirations from rapidly-exploring random tree star (RRT*) with a tournament selection method and informative heuristics to achieve efficient searching of informative data in continuous space. Results of numerical experiments and proof-of-concept field experiments demonstrate the effectiveness and superiority of the proposed RAST* over rapidly-exploring random sampling tree star (RRST*), rapidly-exploring adaptive sampling tree (RAST), and particle swarm optimization (PSO).https://www.mdpi.com/1424-8220/20/9/2515path planningunmanned marine vehiclesadaptive ocean samplingrapidly-exploring adaptive sampling tree star |
| spellingShingle | Chengke Xiong Hexiong Zhou Di Lu Zheng Zeng Lian Lian Caoyang Yu Rapidly-Exploring Adaptive Sampling Tree*: A Sample-Based Path-Planning Algorithm for Unmanned Marine Vehicles Information Gathering in Variable Ocean Environments Sensors path planning unmanned marine vehicles adaptive ocean sampling rapidly-exploring adaptive sampling tree star |
| title | Rapidly-Exploring Adaptive Sampling Tree*: A Sample-Based Path-Planning Algorithm for Unmanned Marine Vehicles Information Gathering in Variable Ocean Environments |
| title_full | Rapidly-Exploring Adaptive Sampling Tree*: A Sample-Based Path-Planning Algorithm for Unmanned Marine Vehicles Information Gathering in Variable Ocean Environments |
| title_fullStr | Rapidly-Exploring Adaptive Sampling Tree*: A Sample-Based Path-Planning Algorithm for Unmanned Marine Vehicles Information Gathering in Variable Ocean Environments |
| title_full_unstemmed | Rapidly-Exploring Adaptive Sampling Tree*: A Sample-Based Path-Planning Algorithm for Unmanned Marine Vehicles Information Gathering in Variable Ocean Environments |
| title_short | Rapidly-Exploring Adaptive Sampling Tree*: A Sample-Based Path-Planning Algorithm for Unmanned Marine Vehicles Information Gathering in Variable Ocean Environments |
| title_sort | rapidly exploring adaptive sampling tree a sample based path planning algorithm for unmanned marine vehicles information gathering in variable ocean environments |
| topic | path planning unmanned marine vehicles adaptive ocean sampling rapidly-exploring adaptive sampling tree star |
| url | https://www.mdpi.com/1424-8220/20/9/2515 |
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