Task allocation of multiple autonomous underwater vehicles based on improved ant colony algorithm

  Objectives  With the submarine topographic survey mission of the Multiple Autonomous Underwater Vehicles (MAUV) as the background, the optimal task allocation method of MAUV is proposed on the basis of an improved ant colony algorithm.

  Methods  The task allocation model is first established and the basic ant colony algorithm subsequently improved. The improved ant colony consists of multiple groups. In order to enhance the adaptive and global search ability of the algorithm, the ant selection method of the remaining task execution capability, new heuristic function and updated global pheromone method are improved. In local searches, the convergence rate of the optimal solution is further accelerated by the 2-opt algorithm.

  Results  The Matlab simulation results show that the improved ant colony algorithm can effectively improve the task allocation efficiency of MAUV while also providing a good balance between the distance of the voyage and the cost of the consumption.

  Conclusions  This article can provide references for submarine topographic survey mission assignment in real environments.