Full Area Coverage of Unmanned Vessels Based on Improved Genetic Algorithm

To address the issue of full area coverage for un-manned vessels navigating through complex static obstacles, this study investigates the optimization of path length and repetition rate. The approach begins with expanding the obstacles on a grid map, followed by partitioning the area into rectangular sections. Traversal order between these partitions is then planned using an enhanced genetic algorithm, which is further integrated with the reciprocating plowing method to achieve complete area coverage. The improvements in the algorithm focus on three key aspects: local optimization of the initial population, the introduction of dynamic parameters, and the application of reversal operations. Simulation experiments demonstrate that this enhanced algorithm not only converges more rapidly compared to traditional methods but also significantly reduces both the path length and repetition rate for full area coverage. This research offers a solution for unmanned vessels to achieve full area coverage in environments with complex static obstacles.