Path Planning of Mission-driven USVs Based on Stochastic Schemata Exploiter

In recent years, the research on unmanned surface vessels (USVs) has become a new hot spot in the development of intelligent applications in the shipping industry of various countries. With the gradual maturity and popularization of modern information technologies such as computer technology and artificial intelligence, the research on USVs has scientific and technological support, and is developing from theoretical research to application practice. Among them, the autonomous assignment and decision-making coordination of multi-target tasks is one of the important research topics for autonomous navigation of USVs. At the same time, the path planning of UAVs also plays a crucial role in autonomous assignment and navigation. In view of the above points, this paper proposes a heuristic optimization algorithm based on stochastic schemata exploiter (SSE). It is used to solve the path optimization problem of USVs for mission requirements. In the experiment, two combinations of two USVs are considered, and the paths are optimized in the two cases of jointly completing the task and cooperatively completing the task. The algorithm combines the heuristic information of the distribution structure of static obstacles in the environment, expands the obstacles and optimizes the node selection of the USV, so that the USV can avoid the obstacles with a higher probability and find an excellent feasible path, and improve the global search ability. Compared with the genetic algorithm (GA) through simulation experiments, the proposed algorithm presents a significant improvement in timeliness and path optimization.