  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.
