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 G1 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.
W. Cai et al., "Task Assignment and Path Planning for Multiple Autonomous Underwater Vehicles using 3D Dubins Curves," Sensors (Switzerland), vol. 17, no. 7, MDPI AG, Jul 2017.
The definitive version is available at https://doi.org/10.3390/s17071607
Electrical and Computer Engineering
Natural Science Foundation of Zhejiang Province
National Natural Science Foundation (China)
National Science Foundation (U.S.)
Keywords and Phrases
Energy balance; Genetic algorithms; Motion planning; Problem solving; Target tracking; Wireless sensor networks; Dynamics constraints; Linear Interpolation; Multiple autonomous underwater vehicles; Multiple AUVs; Path planning problems; Task assignment; Threedimensional (3-d); Underwater wireless sensor networks; Autonomous underwater vehicles
International Standard Serial Number (ISSN)
Article - Journal
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01 Jul 2017