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 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 change 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. AUV's yaw angle is limited, which result in new assignments to the targets. Computation flow is designed so that the algorithm in MATLAB and Python can realizes the path planning to multiple targets. Finally, simulation results prove that the proposed algorithm can effectively accomplish the task assignment task for multi-AUV system.

翻译：针对运动学约束（即欠驱动AUV等载体转向能力受限）下的多AUV系统任务分配问题，本文提出一种融合Dubins路径算法与改进型自组织映射（SOM）神经网络算法的任务分配方法。首先，基于负载均衡与邻域函数，采用改进SOM神经网络方法将目标任务分配给各AUV。当存在导致轨迹规划失败的运动学约束或障碍物时，通过调整SOM神经网络权值进行任务重分配，直至所有AUV均能获得抵达各目标点的可行路径。随后，在若干受限情况下生成Dubins路径。由于AUV偏航角受限，需对目标点进行重新分配。通过设计计算流程，使MATLAB与Python实现的算法能够完成多目标路径规划。仿真结果表明，所提算法可有效实现多AUV系统的任务分配。