Robot path planning based on improved dung beetle optimizer algorithm

In this paper, an improved dung beetle optimization algorithm (IDBO) combined with dynamic window approach (DWA) is proposed for the path planning problem in static and dynamic environments. This method mathematically models the rolling, breeding, foraging, and stealing behaviors of dung beetles. To address the constraints of the conventional dung beetle optimizer in path planning, four improvement facets are proposed to augment the algorithm's efficacy. First, to enhance the search randomness and diversity, an initial population initialization method using Chebyshev chaos map is introduced. Then, curve adaptive golden sine strategy (CGSS) is used to replace the rolling dung beetle position update formulation to increase convergence rate and accuracy during the search. Third, the position update formula for reproductive and foraging dung beetles was improved by using the Levy flights with Cauchy-t mutation strategy (LCTS) to increase the exploratory power and adaptability of the search. Finally, dynamic weight coefficient is introduced to adjust the stealing behavior formulation in order to improve the adaptability and robustness of the algorithm to different problems. The improved algorithm exhibits remarkable enhancements in search efficiency and solution quality by utilizing test functions and experimentally validating the path planning problem. Compared with the traditional dung beetle optimization algorithm with other optimization algorithms, the improved algorithm can converge to the optimal solution faster and has better global search capability and stability.