Path Planning for Searching Missing Submarines Using Heatmaps and Adaptive Algorithms: Collaboration Between Single Searchers and Multiple Searchers

This study proposes a heatmap-based adaptive search path planning method for missing submersibles, integrating dynamic strategy adjustment and multi-agent collaboration to significantly enhance search efficiency. The framework first models the missing submersible's positional distribution under coupled influences of ocean currents, terrain, and seawater salinity, generating a dynamically perturbed heatmap through stochastic simulation. Subsequently, an adaptive search algorithm combining local exploration with global leap strategies optimizes individual search paths based on real-time heatmap feedback. A multi-agent cooperative algorithm is developed, employing dynamic task allocation and coordinated search strategies to amplify overall operational efficiency. Notably, the concept of “optimal number of search agents” is introduced, quantifying the marginal utility between agent quantity and time cost through parametric analysis. This methodology provides scientific decision support for resource deployment in practical rescue operations.