Hybrid path planning algorithm for robots based on modified golden jackal optimization method and dynamic window method

Abstract

Traditional path planning algorithms still face significant challenges in large-scale scenarios with high-density irregular obstacles, such as low search efficiency, limited obstacle avoidance capabilities, and a tendency to get trapped in local optimum. To overcome these challenges, a hybrid route planning algorithm combining the Modified Golden Jackal Optimization (MGJO) algorithm and the Improved Dynamic Window Approach (IDWA) is proposed. To resolve the issue of getting trapped in local optimum and enhance global search efficiency in global path planning, the MGJO algorithm is synthesized based on nonlinear energy attenuation, diverse search strategies, and a guiding mechanism inspired by African vultures. To improve obstacle avoidance efficiency and ensure smoother local paths, the IDWA algorithm is redesigned by optimizing the obstacle distance evaluation function. In global path planning, the MGJO algorithm is evaluated against some state-of-the-art optimizers on 23 benchmark functions. In three different environments, the average path length of the MGJO algorithm over the original algorithm is improved by 10.76%, 16.72% and 25.46%. In local path planning experiments for mobile robots, the IDWA algorithm avoids the local optimum in small and medium-sized maps. In large maps, it significantly reduces the number of the local optimum occurrences, from 6 times to 2 times. The feasibility of the algorithm is validated in real-world experiments.