Application of Ant Colony Optimization Algorithm Based on Triangle Inequality Principle and Partition Method Strategy in Robot Path Planning

Path planning is an important area of mobile robot research, and the ant colony optimization algorithm essential for analyzing path planning. However, current applied to robots still has some limitations, including early blind search, slow convergence speed, more turns. To overcome these problems, improved proposed in this paper. In algorithm, we introduce idea triangle inequality a pseudo-random state transfer strategy enhance guidance target points improve search efficiency quality algorithm. addition, propose pheromone update based on partition method with upper lower limits concentration. This can not only global capability speed but also avoid premature stagnation phenomenon during search. prevent ants from getting into deadlock state, backtracking mechanism enable explore solution space better. Finally, verify effectiveness compared 11 existing methods solving problem, several ACO variants two commonly used algorithms (A* Dijkstra algorithm), experimental results show that plan paths faster convergence, shorter lengths, higher smoothness. Specifically, produces shortest length standard deviation zero while ensuring most rapid highest smoothness case four different grid environments. These demonstrate