Resumen
The precision and efficiency of multi-target path planning are crucial factors influencing the performance of anti-mine operations using unmanned underwater vehicles (UUVs). Addressing the inadequacies in computation time and solution quality present in existing path planning algorithms, this study proposes a novel path cost estimation strategy based on neural networks. This strategy swiftly generates an accurate cost matrix, ensuring the attainment of high-quality traversal orders when utilized as input for the traveling salesman problem, thereby yielding a globally optimal path. Simulation experiments demonstrate that while maintaining high-quality solutions, the proposed strategy significantly enhances the computational efficiency of the algorithm. Furthermore, the practical application and effectiveness of the proposed algorithm have been demonstrated through an actual UUV prototype experiment in a lake environment.