Research on Intelligent Collision Avoidance and Obstacle Avoidance of Unmanned Surface Vehicle in Multitarget Encounter Scenario

With the rapid development of Internet of Things (IoT) technology, Unmanned Surface Vehicles (USVs), as critical nodes in distributed maritime sensing networks, have seen their autonomous collision avoidance capabilities become central to enabling coordination among multiple devices and real-time decision-making. Based on this, this paper presents an intelligent strategy for collision and obstacle avoidance in USVs during multi-target encounter scenarios. A collision risk model based on the International Regulations for Preventing Collisions at Sea (COLREGs) and common practices of sailors is constructed, with it being used as a constraint for the differential evolution algorithm (DE). The objective function is decomposed geometrically and in terms of states, transforming the evaluation of the entire path into evaluating individual path points. In this way, high-quality path points are fully utilized, and a fitness function is built based on each individual path point. The population initialization operation of the DE algorithm is improved through a chaotic multi-population parallel optimization strategy, with a chaotic matrix being introduced to enhance search traversal. Additionally, a parameter randomization strategy is introduced in mutation and crossover operations to avoid local optima, and each subpopulation is optimized in parallel to obtain the best collision avoidance route. Finally, the simulation experiments results demonstrate that the improved DE algorithm demonstrates superior performance in both collision avoidance efficiency and path optimization, confirming the effectiveness of the approach in complex multi-target encounter scenarios.