Mobile Robot Path Planning Based on Improved Ant Colony and Dynamic Window Approach

Path planning is the key to realize the autonomous navigation of mobile robots. Aiming at the problems such as low search efficiency of traditional ant colony algorithm, proneness to falling into local optimal and insufficient dynamic obstacle avoidance ability, a path optimization method combining improved ant colony and dynamic window approach (DWA) is proposed to realize global path optimization and improve local dynamic obstacle avoidance ability of mobile robots. In the global path planning, the heuristic function is established by introducing the artificial potential field factor to enhance the orientation of ants to the target point in the process of path searching, so as to accelerate the search speed of the algorithm. Secondly, in the pheromone update strategy, the extra increment of pheromone concentration difference between the optimal path and the worst path in the previous generation is added to adaptively update the pheromone concentration and enhance the global optimization ability of the algorithm. Then, the triangular pruning method is used to delete the redundant transition nodes of the global path and shorten the path length. Finally, cubic B-spline curve is introduced to optimize the path inflection point and improve the path smoothness. In the local path, an obstacle avoidance cost subfunction considering the speed factor is added to the evaluation function of DWA to increase the local dynamic obstacle avoidance ability of the algorithm, so that the robot can detect and avoid obstacles in real time while moving. The simulation results show that the proposed ant colony algorithm with DWA can improve the path length, iteration times, number of turning points and path smoothness compared with the traditional algorithm, and can effectively improve the dynamic obstacle avoidance ability.