Research on Path Planning for Substation Inspection Robots based on Hybrid Intelligent Optimization Algorithms

Abstract

With the expansion of power grids and the trend toward unmanned substation operations, substation inspection robots are increasingly used, making inspection efficiency a key performance metric. However, traditional intelligent optimization algorithms often suffer from slow convergence or limited global search capabilities in path planning. To address these issues, this paper proposes a hybrid method combining Genetic Algorithm (GA) and Ant Colony Optimization (ACO). First, the principles and characteristics of GA and ACO are analyzed. Then, a global path planning method based on their fusion is designed. Experimental results show that the hybrid algorithm outperforms traditional methods, planning better paths and achieving faster convergence, significantly improving path planning efficiency and global search capability.