Mobile Robot Path Planning Based on Time Taboo Ant Colony Optimization in Dynamic Environment - Xiong, Ni; Zhou, Xinzhi; Yang, Xiuqing; Xiang, Yong; Ma, Junyong (Frontiers in Neurorobotics, 2021)
Aiming at the problems of slow convergence speed, poor global search ability, and unknown time-varying dynamic obstacles in path planning of ant colony algorithm in a dynamic environment, an improved ant colony optimization algorithm using time taboo strategy, namely time taboo ant colony algorithm (TTACO),is proposed. Use adaptive initial pheromone distribution, rollback strategy, pheromone preferential limit update to improve the algorithm's convergence speed and global search ability. For the poor global search ability of the algorithm and the unknown time-varying problem of dynamic obstacles in a dynamic environment, a time taboo strategy is first proposed, and on the basis of this strategy, a three-step arbitration method is proposed to improve the algorithm's global search ability. For the problem of unknown time-varying dynamic obstacles, an Occupancy grid prediction model is proposed based on the time taboo strategy to solve the problem of dynamic obstacle avoidance. In order to improve the algorithm's calculation speed when avoiding obstacles, an ant colony information inheritance mechanism is established. Finally, the algorithm is used to conduct dynamic and static simulation experiments in a simulated factory environment, and compared with similar algorithms. The experimental results show that the time taboo ant colony algorithm can obtain a better path and accelerate the algorithm convergence speed in a static environment, And can successfully avoid dynamic obstacles in a dynamic environment.