A prior information‐based coverage path planner for underwater search and rescue using autonomous underwater vehicle (AUV) with side‐scan sonar
Abstract The coverage path planning (CPP) technique attracts growing interest in studies on underwater search and rescue (SAR) conducted with an autonomous underwater vehicle (AUV) equipped with a side‐scan sonar (SSS). In SAR missions, prior information is crucial. Aiming at the underwater SAR miss...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-07-01
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| Series: | IET Radar, Sonar & Navigation |
| Online Access: | https://doi.org/10.1049/rsn2.12256 |
| _version_ | 1811249593343541248 |
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| author | Chang Cai Jianfeng Chen Qingli Yan Fen Liu Rongyan Zhou |
| author_facet | Chang Cai Jianfeng Chen Qingli Yan Fen Liu Rongyan Zhou |
| author_sort | Chang Cai |
| collection | DOAJ |
| description | Abstract The coverage path planning (CPP) technique attracts growing interest in studies on underwater search and rescue (SAR) conducted with an autonomous underwater vehicle (AUV) equipped with a side‐scan sonar (SSS). In SAR missions, prior information is crucial. Aiming at the underwater SAR mission with prior information, a new coverage path planner (SAR‐A*) is proposed. The ultimate goal is to generate a feasible path for completely covering the task area and preferentially visiting more valuable cells with fewer turns. First, the whole task area is decomposed into hexagon cells as waypoints to be visited for complete coverage. Second, the probability of discovering the target is obtained according to the target presence probability and the SSS detection ability. Under the assumption of prior target information, the target presence probability is modelled as a two‐dimension Gaussian distribution based on predicted target locations or trajectories. Then, an optimal next‐waypoint selection process is formulated as a multi‐objective decision‐making problem and solved by the weighted metric method. Finally, simulation and experimental results demonstrate that the generated path can improve the cumulative probability of discovering the target with fewer turns. |
| first_indexed | 2024-04-12T15:49:16Z |
| format | Article |
| id | doaj.art-055b577d405f431fb936c9aaf8b94916 |
| institution | Directory Open Access Journal |
| issn | 1751-8784 1751-8792 |
| language | English |
| last_indexed | 2024-04-12T15:49:16Z |
| publishDate | 2022-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Radar, Sonar & Navigation |
| spelling | doaj.art-055b577d405f431fb936c9aaf8b949162022-12-22T03:26:33ZengWileyIET Radar, Sonar & Navigation1751-87841751-87922022-07-011671225123910.1049/rsn2.12256A prior information‐based coverage path planner for underwater search and rescue using autonomous underwater vehicle (AUV) with side‐scan sonarChang Cai0Jianfeng Chen1Qingli Yan2Fen Liu3Rongyan Zhou4School of Marine Science and Technology Northwestern Polytechnical University Xi'an ChinaSchool of Marine Science and Technology Northwestern Polytechnical University Xi'an ChinaSchool of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an ChinaSchool of Marine Science and Technology Northwestern Polytechnical University Xi'an ChinaSchool of Marine Science and Technology Northwestern Polytechnical University Xi'an ChinaAbstract The coverage path planning (CPP) technique attracts growing interest in studies on underwater search and rescue (SAR) conducted with an autonomous underwater vehicle (AUV) equipped with a side‐scan sonar (SSS). In SAR missions, prior information is crucial. Aiming at the underwater SAR mission with prior information, a new coverage path planner (SAR‐A*) is proposed. The ultimate goal is to generate a feasible path for completely covering the task area and preferentially visiting more valuable cells with fewer turns. First, the whole task area is decomposed into hexagon cells as waypoints to be visited for complete coverage. Second, the probability of discovering the target is obtained according to the target presence probability and the SSS detection ability. Under the assumption of prior target information, the target presence probability is modelled as a two‐dimension Gaussian distribution based on predicted target locations or trajectories. Then, an optimal next‐waypoint selection process is formulated as a multi‐objective decision‐making problem and solved by the weighted metric method. Finally, simulation and experimental results demonstrate that the generated path can improve the cumulative probability of discovering the target with fewer turns.https://doi.org/10.1049/rsn2.12256 |
| spellingShingle | Chang Cai Jianfeng Chen Qingli Yan Fen Liu Rongyan Zhou A prior information‐based coverage path planner for underwater search and rescue using autonomous underwater vehicle (AUV) with side‐scan sonar IET Radar, Sonar & Navigation |
| title | A prior information‐based coverage path planner for underwater search and rescue using autonomous underwater vehicle (AUV) with side‐scan sonar |
| title_full | A prior information‐based coverage path planner for underwater search and rescue using autonomous underwater vehicle (AUV) with side‐scan sonar |
| title_fullStr | A prior information‐based coverage path planner for underwater search and rescue using autonomous underwater vehicle (AUV) with side‐scan sonar |
| title_full_unstemmed | A prior information‐based coverage path planner for underwater search and rescue using autonomous underwater vehicle (AUV) with side‐scan sonar |
| title_short | A prior information‐based coverage path planner for underwater search and rescue using autonomous underwater vehicle (AUV) with side‐scan sonar |
| title_sort | prior information based coverage path planner for underwater search and rescue using autonomous underwater vehicle auv with side scan sonar |
| url | https://doi.org/10.1049/rsn2.12256 |
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