Rescue plan of unmanned life-saving vehicle in multi-obstacle waters under multi-strategy innovative ant colony optimization strategy

To improve the rescue efficiency of the jet propulsion unmanned life-saving vehicles (ULSVs), a novel rescue plan based on improved Ant Colony Optimization (ACO) for multiple ULSVs in multi-obstacle environments is proposed in this research. In the proposed approach, an unevenly initialized pheromone matrix is first employed to help the ant colony search a larger space and maintain population diversity. Secondly, considering the issues of poor search efficiency and accuracy in traditional ACO, this study introduces a position update strategy based on exponential approximation and dynamic tracking of optimal values. Meanwhile, a grid partitioning method based on local grid refinement is designed to further enhance the quality of the planned paths and ensure safety. Finally, a dynamic adaptive optimization mechanism is proposed to endow the ACO with the path planning ability in dynamic environments, enabling it to quickly adapt when the environment changes. In the experimental stage, a large number of simulation experiments and practical tests are carried out, and real-world maps and ocean current data are selected to validate the proposed strategy. The results show that the rescue system can plan paths that best meet actual needs and offer higher safety and reliability compared to other algorithms.