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

摘要

Aiming at the shortcomings of ant colony algorithm in the complex environment, such as being easy to fall into local optimum and difficult to guarantee real-time path planning of robots, this paper proposes a dynamic window algorithm based on improved ant colony (IACO-DWA). In order to avoid the blind search of ants in the early stage, this method designs an adaptive distance induction factor, and combines the maximum and minimum ant system (MMAS) to improve the pheromone update rule to prevent falling into the local optimum; to improve the probability transfer rule by constructing a corner suppression factor, Reduce the path inflection points, and integrate the global path points generated by the DWA tracking ant colony to construct a new position evaluation function, and then plan a smooth path trajectory. The simulation results show that the method in this paper strengthens the optimization performance of the global path while realizing the local dynamic obstacle avoidance.