Task Assignment and Path Planning for Multiple Autonomous Underwater Vehicles Using 3D Dubins Curves †
This paper investigates the task assignment and path planning problem for multiple AUVs in three dimensional (3D) underwater wireless sensor networks where nonholonomic motion constraints of underwater AUVs in 3D space are considered. The multi-target task assignment and path planning problem is mod...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-07-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/17/7/1607 |
| _version_ | 1828282968365334528 |
|---|---|
| author | Wenyu Cai Meiyan Zhang Yahong Rosa Zheng |
| author_facet | Wenyu Cai Meiyan Zhang Yahong Rosa Zheng |
| author_sort | Wenyu Cai |
| collection | DOAJ |
| description | This paper investigates the task assignment and path planning problem for multiple AUVs in three dimensional (3D) underwater wireless sensor networks where nonholonomic motion constraints of underwater AUVs in 3D space are considered. The multi-target task assignment and path planning problem is modeled by the Multiple Traveling Sales Person (MTSP) problem and the Genetic Algorithm (GA) is used to solve the MTSP problem with Euclidean distance as the cost function and the Tour Hop Balance (THB) or Tour Length Balance (TLB) constraints as the stop criterion. The resulting tour sequences are mapped to 2D Dubins curves in the X − Y plane, and then interpolated linearly to obtain the Z coordinates. We demonstrate that the linear interpolation fails to achieve G 1 continuity in the 3D Dubins path for multiple targets. Therefore, the interpolated 3D Dubins curves are checked against the AUV dynamics constraint and the ones satisfying the constraint are accepted to finalize the 3D Dubins curve selection. Simulation results demonstrate that the integration of the 3D Dubins curve with the MTSP model is successful and effective for solving the 3D target assignment and path planning problem. |
| first_indexed | 2024-04-13T08:37:35Z |
| format | Article |
| id | doaj.art-3bd843fe22064d2a990c608e8152e3ca |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T08:37:35Z |
| publishDate | 2017-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3bd843fe22064d2a990c608e8152e3ca2022-12-22T02:54:01ZengMDPI AGSensors1424-82202017-07-01177160710.3390/s17071607s17071607Task Assignment and Path Planning for Multiple Autonomous Underwater Vehicles Using 3D Dubins Curves †Wenyu Cai0Meiyan Zhang1Yahong Rosa Zheng2School of Electronics & Information, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Electrical Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, ChinaDepartment of Electrical & Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USAThis paper investigates the task assignment and path planning problem for multiple AUVs in three dimensional (3D) underwater wireless sensor networks where nonholonomic motion constraints of underwater AUVs in 3D space are considered. The multi-target task assignment and path planning problem is modeled by the Multiple Traveling Sales Person (MTSP) problem and the Genetic Algorithm (GA) is used to solve the MTSP problem with Euclidean distance as the cost function and the Tour Hop Balance (THB) or Tour Length Balance (TLB) constraints as the stop criterion. The resulting tour sequences are mapped to 2D Dubins curves in the X − Y plane, and then interpolated linearly to obtain the Z coordinates. We demonstrate that the linear interpolation fails to achieve G 1 continuity in the 3D Dubins path for multiple targets. Therefore, the interpolated 3D Dubins curves are checked against the AUV dynamics constraint and the ones satisfying the constraint are accepted to finalize the 3D Dubins curve selection. Simulation results demonstrate that the integration of the 3D Dubins curve with the MTSP model is successful and effective for solving the 3D target assignment and path planning problem.https://www.mdpi.com/1424-8220/17/7/1607target trackingtask assignmentmultiple AUVsenergy balancegenetic algorithm |
| spellingShingle | Wenyu Cai Meiyan Zhang Yahong Rosa Zheng Task Assignment and Path Planning for Multiple Autonomous Underwater Vehicles Using 3D Dubins Curves † Sensors target tracking task assignment multiple AUVs energy balance genetic algorithm |
| title | Task Assignment and Path Planning for Multiple Autonomous Underwater Vehicles Using 3D Dubins Curves † |
| title_full | Task Assignment and Path Planning for Multiple Autonomous Underwater Vehicles Using 3D Dubins Curves † |
| title_fullStr | Task Assignment and Path Planning for Multiple Autonomous Underwater Vehicles Using 3D Dubins Curves † |
| title_full_unstemmed | Task Assignment and Path Planning for Multiple Autonomous Underwater Vehicles Using 3D Dubins Curves † |
| title_short | Task Assignment and Path Planning for Multiple Autonomous Underwater Vehicles Using 3D Dubins Curves † |
| title_sort | task assignment and path planning for multiple autonomous underwater vehicles using 3d dubins curves † |
| topic | target tracking task assignment multiple AUVs energy balance genetic algorithm |
| url | https://www.mdpi.com/1424-8220/17/7/1607 |
| work_keys_str_mv | AT wenyucai taskassignmentandpathplanningformultipleautonomousunderwatervehiclesusing3ddubinscurves AT meiyanzhang taskassignmentandpathplanningformultipleautonomousunderwatervehiclesusing3ddubinscurves AT yahongrosazheng taskassignmentandpathplanningformultipleautonomousunderwatervehiclesusing3ddubinscurves |