A novel path planning method for wheel-legged unmanned vehicles based on improved ant colony algorithm

Wheel-legged unmanned vehicles combine the efficiency of wheeled mobility with the terrain adaptability of legged mobility, and perform well in the exploration, rescue and other tasks of complex mountain terrain. The successful completion of tasks in complex mountain terrain depends on good perception, decision and planning of the wheel-legged unmanned vehicles, among which excellent path planning will help to improve the efficiency of passing. A novel path planning method based on improved ant colony algorithm for wheel-legged unmanned vehicles is proposed in this paper. Combined with the mechanical structure characteristics of wheel-legged vehicles, the obstacle crossing parameters are added to the heuristic function to relax the obstacle constraint in the path search, which is an improvement of the path planning algorithm. Through a 3D terrain map to carry out simulation experiments, the results show that the improved ant colony algorithm has faster convergence speed, and the search path is closer to the linear distance.