Chaotic heuristic assisted method for the search path planning of the multi-BWBUG cooperative system

Abstract

Blended-wing-body underwater gliders (BWBUGs) are low cost and have high endurance. As such, they are highly suitable for underwater search missions. However, the search path planning of BWBUGs presents significant challenges due to the complexities in designing the planning model and solution algorithm. In this study, we propose a novel chaotic heuristic assisted method (CHAM) for search missions involving multi-BWBUG cooperative systems. First, our approach addresses the challenges by introducing a probabilistic sensing model that accurately describes the detection performance of the BWBUG. Then, a search path planning model of the multi-BWBUG cooperative system is developed on the basis of the cumulative detection probability and communication energy consumption ratio. Second, we propose the sine-cosine chaos strategy, which exhibits earlier chaotic state entry and higher ergodicity compared to the sine chaos strategy. Leveraging this, CHAM integrates the sine-cosine chaos strategy with a well-designed hierarchical collaboration mechanism. The proposed algorithm adapts different position update strategies at various stages to enhance convergence speed and avoid local optima. Finally, the simulation experiments and statistical analysis results provide compelling evidence that CHAM excels in performing search path planning for the multi-BWBUG cooperative system, and the comprehensive performance of CHAM is better than that of comparison algorithms.