To address the common limitations of conventional dual-layer path planning methods, such as slow global convergence, delayed local obstacle avoidance response, and insufficient inter-layer integration, this paper proposes an enhanced collaborative planning framework combining the Improved Dung Beetle Optimizer (IDBO) and the Improved Dynamic Window Approach (IDWA). First, the proposed IDBO solves the problems of population aggregation and unbalanced exploration–exploitation of traditional algorithms by optimizing the initialization strategy and reconstructing the position update mechanism. Second, in the local path planning stage, the IDWA introduces an adaptive evaluation function embedded with obstacle motion prediction and a global path-tracking factor, which breaks through the limitations of traditional local algorithms, such as fixed weights and lack of environmental adaptability, while resolving the contradictions of poor inter-layer coupling and path redundancy in traditional dual-layer frameworks. The results of comparative simulation experiments show that the average path length is reduced by 6.5% and the running time is decreased by 9.1%. This framework effectively overcomes the problems of delayed local response and insufficient inter-layer integration in traditional dual-layer path planning.
Zhou et al. (Sun,) studied this question.