Trajectory Planning Method for Multi-UUV Formation Rendezvous in Obstacle and Current Environments
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| Publicado no: | Journal of Marine Science and Engineering vol. 13, no. 12 (2025), p. 2221-2245 |
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| Outros Autores: | , |
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MDPI AG
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| Acesso em linha: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2077-1312 | ||
| 024 | 7 | |a 10.3390/jmse13122221 |2 doi | |
| 035 | |a 3286311084 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231479 |2 nlm | ||
| 100 | 1 | |a Chen, Tao | |
| 245 | 1 | |a Trajectory Planning Method for Multi-UUV Formation Rendezvous in Obstacle and Current Environments | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Formation rendezvous is a critical phase during the deployment or recovery of multiple unmanned underwater vehicles (UUVs) in cooperative missions, and represents one of the core problems in multi-UUV cooperative planning. In practical marine environments with obstacles and currents, multiple constraints must be simultaneously satisfied, including obstacle avoidance, inter-UUV collision prevention, kinematic limitations, and specified initial and terminal states. These requirements make energy-optimal trajectory planning for multi-UUV formation rendezvous highly challenging. Traditional integrated cooperative planning methods often struggle to obtain optimal or even feasible solutions due to the complexity of constraints and the vastness of the solution space. To address these issues, a dual-layer planning framework for multi-UUV formation rendezvous trajectory planning in environments with obstacles and currents is proposed in this paper. The framework consists of an initial individual trajectory planning layer and a secondary cooperative planning layer. In the initial individual trajectory planning stage, the Grey Wolf Optimization (GWO) algorithm is employed to optimize high-order terms of polynomial curves, generating initial trajectories for individual UUVs that satisfy obstacle avoidance, kinematic constraints, and state requirements. These trajectories are then used as inputs to the secondary cooperative planning stage. In the cooperative stage, a Self-Adaptive Particle Swarm Optimization (SAPSO) is introduced to explicitly address inter-UUV collision avoidance while incorporating all individual constraints, ultimately producing a cooperative rendezvous trajectory that minimizes overall energy consumption. To validate the effectiveness of the proposed method, a simulation environment incorporating vortex flow fields and real-world island topography was constructed. Simulation results demonstrate that the proposed hierarchical trajectory planning method is capable of generating energy-optimal formation rendezvous trajectories that satisfy multiple constraints for multi-UUV systems in environments with obstacles and ocean currents, highlighting its strong potential for practical engineering applications. | |
| 653 | |a Islands | ||
| 653 | |a Kinematics | ||
| 653 | |a Marine environment | ||
| 653 | |a Particle swarm optimization | ||
| 653 | |a Collaboration | ||
| 653 | |a Unmanned underwater vehicles | ||
| 653 | |a Collision avoidance | ||
| 653 | |a Optimization | ||
| 653 | |a Ocean currents | ||
| 653 | |a Polynomials | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Rendezvous trajectories | ||
| 653 | |a Objectives | ||
| 653 | |a Energy consumption | ||
| 653 | |a Underwater vehicles | ||
| 653 | |a Efficiency | ||
| 653 | |a Vehicles | ||
| 653 | |a Autonomous underwater vehicles | ||
| 653 | |a Wind power | ||
| 653 | |a Trajectory optimization | ||
| 653 | |a Planning | ||
| 653 | |a Energy | ||
| 653 | |a Solution space | ||
| 653 | |a Algorithms | ||
| 653 | |a Constraints | ||
| 653 | |a Trajectory planning | ||
| 653 | |a Obstacle avoidance | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Wang, Kai | |
| 700 | 1 | |a Wang Qingzhe | |
| 773 | 0 | |t Journal of Marine Science and Engineering |g vol. 13, no. 12 (2025), p. 2221-2245 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3286311084/abstract/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3286311084/fulltextwithgraphics/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3286311084/fulltextPDF/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |