AUV path planning method based on improved sparrow search algorithm

ObjectivesAddressing the issues of suboptimal planning results and unstable path searches in three-dimensional path planning algorithms for Autonomous Underwater Vehicle (AUV) in complex underwater environments, an innovative AUV path planning method based on an improved Sparrow Search Algorithm is proposed. MethodsThe innovation of the new algorithm comprises the derivation of a vector analysis method for evaluating interval responses, the introduction of segmented learning and quantum computing mechanisms, the improvement of the update formula for the classic Sparrow Search Algorithm, and the updating of population quantities through Thompson sampling. Simulation tests are conducted in complex ocean currents. Results The results indicate that the proposed improved algorithm reduces the average maximum time by 49.88% compared to the previous version. In extreme and abrupt ocean current environments, the failure rate is reduced by 10.6%. It demonstrates the strong global search capability, optimization performance, and efficient path planning ability of the proposed algorithm. The algorithm exhibits good convergence, making it suitable for underwater path planning in dynamic environments. Conclusions The research findings have significant implications for path planning problems in AUVs and other domains.