Optimizing The Movement Strategies of "Ban Deng Dragon" with APO

Abstract

This study investigates the challenges associated with collision avoidance and path optimization in the spiral coiling motion of the "Ban Deng Dragon" dance team. Current path planning methods, including the enhanced Jump Point Search, A*, and Ant Colony Optimization, still demonstrate limitations in computational efficiency, dynamic adaptability, and handling multiple constraints. A mathematical model of spiral motion, subject to boundary and collision constraints, was developed to determine the minimum pitch requirement. The APO algorithm, inspired by the adaptive flight and foraging behaviors of Arctic puffins, was then employed to iteratively optimize the spiral trajectory. Experimental results revealed that the APO algorithm achieved a minimum feasible pitch of 0.450338 m while ensuring collision avoidance, outperforming both the Genetic Algorithm and Simulated Annealing in terms of convergence speed and robustness. This study highlights the effectiveness of the APO algorithm in solving multi-constrained optimization problems, improving the precision of traditional cultural performances, and showcasing the potential of bio-inspired algorithms in dynamic systems.