An enhanced dung beetle optimizer with adaptive node selection and dynamic step search for mobile robots path planning

Abstract

Abstract Path planning plays a crucial role in determining the shortest and safest route for autonomous mobile robots, and metaheuristic optimization algorithms have demonstrated their efficacy in solving complex problems involving numerous and variably shaped obstacles. The dung beetle optimizer (DBO) emerges as a novel heuristic optimization algorithm, drawing inspiration from the behavior patterns exhibited by dung beetles. This paper presents an improved algorithm called the enhanced DBO (EDBO) designed to address the mobile robot path planning problem. First, a node selection strategy based on the search radius is proposed to reduce the probability of path reciprocation while concurrently enhancing the initial population quality. Second, the conventional ball-rolling strategy employed in DBO is replaced with a dynamic step size search strategy. This strategy dynamically adjusts the step size based on the relative distance between the current position and the worst position of each iteration, enabling the population to update its position adaptively in response to environmental changes. Additionally, the global optimal position is incorporated to guide the search process, thereby improving the global search performance of DBO. Finally, a path fine-tuning strategy is implemented to refine the new generated individuals, with geometric principles of triangles being introduced to locally adjust the optimized path and guide the population out of local optima. Experiments are conducted to validate the effectiveness of the three improvement strategies, comparing the path optimization performance of EDBO with five other algorithms across five different complexity environments. Additionally, two statistical testing methods are employed to evaluate the experimental results. The experimental results indicate that, in all five environments, EDBO outperforms the other five algorithms in terms of path length and achieves superior path smoothness in 80% of the cases. Moreover, EDBO consistently demonstrates better overall performance than DBO, excelling in both convergence accuracy and speed.