Path Planning Method for Omnidirectional Mobile Robots Based on an Improved Hippopotamus Optimization Algorithm

To address the issues of low search accuracy and insufficient stability in mobile robot path planning within complex environments, this paper proposes an improved hippopotamus optimization (IHO) algorithm. During mobile robot waypoint planning, Tent chaotic mapping is introduced during the population initialization stage to improve the uniformity of individual distribution in the search space and enhance population diversity. During the position update stage, a nonlinear adaptive weight factor is incorporated to dynamically balance the global exploration and local exploitation capabilities of the algorithm. In the third stage of the algorithm, a lens opposition-based learning strategy is introduced to improve global optimization performance through symmetric mapping and selection of candidate solutions. Experimental results demonstrate that IHO exhibits superior overall convergence speed and result stability compared to six benchmark algorithms, including hippopotamus optimization (HO). In three complex obstacle environments, IHO enables the robot to generate paths closer to the global optimum, demonstrating its effectiveness in practical path planning applications.