Li Xin, Gan Wenyang, Pang Wen, Zhu Daqi
College of Information Engineering, Shanghai Maritime University, 1550 Haigang Avenue, Pudong New Area, Shanghai 201306, China.
Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China.
Sensors (Basel). 2024 Sep 30;24(19):6345. doi: 10.3390/s24196345.
To deal with the task assignment problem of multi-AUV systems under kinematic constraints, which means steering capability constraints for underactuated AUVs or other vehicles likely, an improved task assignment algorithm is proposed combining the Dubins Path algorithm with improved SOM neural network algorithm. At first, the aimed tasks are assigned to the AUVs by the improved SOM neural network method based on workload balance and neighborhood function. When there exists kinematic constraints or obstacles which may cause failure of trajectory planning, task re-assignment will be implemented by changing the weights of SOM neurals, until the AUVs can have paths to reach all the targets. Then, the Dubins paths are generated in several limited cases. The AUV's yaw angle is limited, which results in new assignments to the targets. Computation flow is designed so that the algorithm in MATLAB and Python can realize the path planning to multiple targets. Finally, simulation results prove that the proposed algorithm can effectively accomplish the task assignment task for a multi-AUV system.
为解决多自主水下航行器（AUV）系统在运动学约束下的任务分配问题，这意味着可能对欠驱动AUV或其他车辆存在转向能力约束，提出了一种将杜宾斯路径算法与改进的自组织映射（SOM）神经网络算法相结合的改进任务分配算法。首先，基于工作量平衡和邻域函数，通过改进的SOM神经网络方法将目标任务分配给AUV。当存在运动学约束或可能导致轨迹规划失败的障碍物时，将通过改变SOM神经元的权重来进行任务重新分配，直到AUV能够有路径到达所有目标。然后，在几种有限情况下生成杜宾斯路径。AUV的偏航角受到限制，这导致对目标的新分配。设计了计算流程，以便MATLAB和Python中的算法能够实现对多个目标的路径规划。最后，仿真结果证明所提出的算法能够有效地完成多AUV系统的任务分配任务。
