This paper solves the problem of cooperative path planning of multiple unmanned surface vehicles (USVs) for search and coverage tasks in water environments. Firstly, taking the search coverage problem of water surface pollutants as an example, the information concentration map is built to predict the diffusion of water surface pollutants. Secondly, we propose a region division method based on a Voronoi diagram, which divides the region and assigns it to each unmanned surface vehicle (USV). Then, on the basis of the traditional Model Predictive Control (MPC), the future reward index based on the regional centroid is introduced, and the Improved Salp Swarm Algorithm (ISSA) is used to solve MPC. Simulation results show the effectiveness of the proposed method.

To solve the problem of traversal multi-target path planning for an unmanned cruise ship in an unknown obstacle environment of lakes, this study proposed a hybrid multi-target path planning algorithm. The proposed algorithm can be divided into two parts. First, the multi-target path planning problem was transformed into a traveling salesman problem, and an improved Grey Wolf Optimization (GWO) algorithm was used to calculate the multi-target cruise sequence. The improved GWO algorithm optimized the convergence factor by introducing the Beta function, which can improve the convergence speed of the traditional GWO algorithm. Second, based on the planned target sequence, an improved D* Lite algorithm was used to implement the path planning between every two target points in an unknown obstacle environment. The heuristic function in the D* Lite algorithm was improved to reduce the number of expanded nodes, so the search speed was improved, and the planning path was smoothed. The proposed algorithm was verified by experiments and compared with the other four algorithms in both ordinary and complex environments. The experimental results demonstrated the strong applicability and high effectiveness of the proposed method.

A single unmanned surface combatant (USV) has poor mission execution capability, so the cooperation of multiple unmanned surface ships is widely used. Cooperative hunting is an important aspect of multi USV collaborative research. Therefore, this paper proposed a cooperative hunting method for multi-USV based on the A* algorithm in an environment with obstacles. First, based on the traditional A* algorithm, a path smoothing method based on USV minimum turning radius is proposed. At the same time, the post order traversal recursive algorithm in the binary tree method is used to replace the enumeration algorithm to obtain the optimal path, which improves the efficiency of the A* algorithm. Second, a biomimetic multi USV swarm collaborative hunting method is proposed. Multiple USV clusters simulate the hunting strategy of lions to pre-form on the target’s path, so multiple USV clusters do not require manual formation. During the hunting process, the formation of multiple USV groups is adjusted to limit the movement and turning of the target, thereby reducing the range of activity of the target and improving the effectiveness of the algorithm. To verify the effectiveness of the algorithm, two sets of simulation experiments were conducted. The results show that the algorithm has good performance in path planning and target search.

In the traditional rapidly exploring random tree (RRT) algorithm, the planned path is not smooth, the distance is long, and the fault tolerance rate of the planned path is low. Disturbances in an environment can cause unmanned surface vessels (USVs) to deviate from their planned path during navigation. Therefore, this paper proposed a path-planning method considering environmental disturbance based on virtual potential field RRT* (VPF-RRT*). First, on the basis of the RRT* algorithm, a VPF-RRT* algorithm is proposed for planning the planning path. Second, an anti-environmental disturbance method based on a deep recurrent neural networks PI (DRNN-PI) controller is proposed to allow the USV to eliminate environmental disturbance and maintain its track along the planning path. Comparative simulation experiments between the proposed algorithm and the other algorithms were conducted within two different experimental scenes. In the path-planning simulation experiment, the VPF-RRT* algorithm had a shorter planning path and a smaller total turning angle when compared to the RRT* algorithm. In the path-tracking simulation experiment, when using the proposed algorithm, the USV could effectively compensate for the impact of environmental disturbance and maintain its navigation along the planning path. In order to avoid the contingency of the experiment and verify the effectiveness and generality of the proposed algorithm, three experiments were conducted. The simulation results verify the effectiveness of the proposed algorithm.