关键词: 路径规划, 非平坦环境, 能量消耗, 防碰撞, 多目标优化

Abstract: To achieve efficient, safe, and energy-saving operations of tracked robots under non-flat environments, an improved non-dominated sorting genetic algorithm Ⅱ（NSGA-Ⅱ) multi-objective path planning method was presented. Firstly, comprehensively analyzing the operational requirements of tracked robots under non-flat environments, a 2.5D grid environmental model was established. Secondly, short path length, low energy consumption, and high safety were selected as sub-objective criteria for path planning. Finally, the 8-domain Manhattan collision avoidance algorithm was employed to improve the grid selection method, avoiding collisions between robots and vertices. Additionally, the NSGA-Ⅱ algorithm introduced an elite replacement strategy to expand the population size, preventing the loss of excellent path genes and accelerating algorithm convergence. Compared to the multi-objective variable neighborhood search（MOVNS) algorithm, the proposed method plans paths with an average reduction of 9.02% in path length, an average energy savings of 18.36%, and an average decrease of 7.28% in danger rate, contributing to the enhancement of path quality under non-flat environments. 

Key words: path planning, non-flat environment, energy consumption, collision-free, multi-objective optimization