Dynamic Navigation and Area Assignment of Multiple USVs Based on Multi-Agent Deep Reinforcement Learning

• Published in: Sensors (Basel, Switzerland) Switzerland), 2022-09, Vol.22 (18), p.6942
• Main Authors: Wen, Jiayi, Liu, Shaoman, Lin, Yejin
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Autonomous underwater vehicles; Autonomy; Communication; Cooperation; Deep learning; Evacuations & rescues; Genetic algorithms; Linear programming; Methods; multi-agent deep reinforcement learning; multi-object optimization; Obstacle avoidance; Optimization; Planning; policy gradient; Surface vehicles; Task complexity; trajectory design; Unmanned aerial vehicles; Unmanned vehicles; USV
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22186942

The unmanned surface vehicle (USV) has attracted more and more attention because of its basic ability to perform complex maritime tasks autonomously in constrained environments. However, the level of autonomy of one single USV is still limited, especially when deployed in a dynamic environment to perform multiple tasks simultaneously. Thus, a multi-USV cooperative approach can be adopted to obtain the desired success rate in the presence of multi-mission objectives. In this paper, we propose a cooperative navigating approach by enabling multiple USVs to automatically avoid dynamic obstacles and allocate target areas. To be specific, we propose a multi-agent deep reinforcement learning (MADRL) approach, i.e., a multi-agent deep deterministic policy gradient (MADDPG), to maximize the autonomy level by jointly optimizing the trajectory of USVs, as well as obstacle avoidance and coordination, which is a complex optimization problem usually solved separately. In contrast to other works, we combined dynamic navigation and area assignment to design a task management system based on the MADDPG learning framework. Finally, the experiments were carried out on the Gym platform to verify the effectiveness of the proposed method.