LAUSR

This manuscript addresses the trajectory optimization for underwater data muling with mobile nodes. In the underwater data muling scenario, multiple autonomous underwater vehicles (AUVs) explore or sample a mission area and autonomous surface vehicles (ASVs) visit underway AUVs to retrieve collected data. The optimization objectives are to simultaneously maximize fairness in data transmissions and minimize the travel distance of the surface nodes. We propose a nearest-K reinforcement learning algorithm. In the algorithm, we choose only from the nearest-K AUVs as candidates for the next node for data transmissions. We choose the distance between AUVs and the ASV as the state, selected AUVs as the action. A reward is designed as the function of both data volume transmitted and the ASV travel distance. In the scenario with multiple ASVs, an AUV association strategy is proposed to support the use of multiple surface nodes. We conduct computer simulations for performance evaluation. The effects from the number of AUVs, the size of the mission area, and state selection are investigated. Simulation results show that the proposed algorithm outperforms traditional methods in terms of fairness and the ASV travel distance.