AUV Path Planning Method for Underwater Moving Target Search Based on a Target-Position-Controlled Mutation Strategy Genetic Algorithm

To accomplish underwater search missions of the target with stochastic motion, path planning is required prior to autonomous underwater vehicle (AUV) operations. Genetic algorithms (GAs) are a classical approach for target search path planning; however, when applied to moving underwater targets, they often rely on oversimplified motion models and lack flexible mutation direction control, resulting in suboptimal search paths and reduced detection performance. To address these limitations, this paper proposes a GA-based path planning method incorporating a target-position-controlled mutation strategy. First, a Markov process combined with a grid-based approach is used to model stochastic target motion and derive the spatial probability distribution of target positions. Second, based on the target distribution grid, the influence of random fluctuations on sonar signals is simulated to construct a probabilistic detection model. Finally, the target distribution and detection probability model are integrated into the proposed GA to generate an optimal AUV search path. Simulation results show that the proposed method effectively improves path planning for moving targets under stochastic conditions, increasing the target cumulative detection probability from 0.33 to 0.48.