Ground Robot Path Planning Based on Simulated Annealing Genetic Algorithm

Robot path planning is the key to robot navigation. We implemented the robot path planning based on ant colony algorithm and genetic algorithm, and proposed simulated annealing genetic algorithm. Under the condition that there is not much difference in running time (within 3 seconds), planning results of different terrains, start and end points based on ant colony algorithm(with 200 iterations)and simulated annealing genetic algorithm show that, the optimal path outputted by simulated annealing genetic algorithm is better than the optimal path outputted by ant colony algorithm in terms of avoiding obstacles; The simulated annealing genetic algorithm has shorter average optimal path length than ant colony algorithm in multiple tests, the average path length is reduced by 6.85%.

I. Introduction

Robot navigation technology is a hot issue in the field of AI. Scholars have made some achievements in the research of robot path planning technology. Grid method [1] is the most widely used and effective path planning method. On the basis of grid method, a large number of algorithms are generated, such as A* algorithm [2], simulated annealing algorithm [3], Dijkstra algorithm [4], Prim algorithm [5], etc.