Robot path planning based on a two-stage DE algorithm and applications

Abstract

To tackle the path planning problem, this study introduced a novel algorithm called two-stage parameter adjustment-based differential evolution (TPADE). This algorithm draws inspiration from group behavior to implement a two-stage scaling factor variation strategy. In the initial phase, it adapts according to environmental complexity. In the following phase, it combines individual and global experiences to fine-tune the orientation factor, effectively improving its global search capability. Furthermore, this study developed a new population update method, ensuring that well-adapted individuals are retained, which enhances population diversity. In benchmark function tests across different dimensions, the proposed algorithm consistently demonstrates superior convergence accuracy and speed. This study also tested the TPADE algorithm in path planning simulations. The experimental results reveal that the TPADE algorithm outperforms existing algorithms by achieving path lengths of 28.527 138 and 31.963 990 in simple and complex map environments, respectively. These findings indicate that the proposed algorithm is more adaptive and efficient in path planning.