in 2008 IEEE intelligent vehicles Symposium 1, 1149–1154. Detection, prediction, and avoidance of dynamic obstacles in urban environ ments. Optimal path planning for mobile robots based on intensified ant colony optimization algorithm. Mobile robot path planning based on ant colony algorithm with A* heuristic. A real time self-tuning motion controller for mobile robot systems. The experimental results show that the TTACO can obtain a better path and accelerate the convergence speed of the algorithm in a static environment and can successfully avoid dynamic obstacles in a dynamic environment.Īnt colony algorithm dynamic environment mobile robot path planning time taboo strategy.Ĭopyright © 2021 Xiong, Zhou, Yang, Xiang and Ma.īoukens M., Boukabou A., Chadli M. Finally, the algorithm is used to conduct dynamic simulation experiments in a simulated factory environment and is compared with other similar algorithms. In order to improve the algorithm's calculation speed when avoiding obstacles, an ant colony information inheritance mechanism is established. For the 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. 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, based on which a three-step arbitration method is put forward to improve its weakness in global search. An improved ant colony optimization algorithm using time taboo strategy is proposed, namely, time taboo ant colony optimization (TTACO), which uses adaptive initial pheromone distribution, rollback strategy, and pheromone preferential limited update to improve the algorithm's convergence speed and global search ability. This article aims to improve the problem of slow convergence speed, poor global search ability, and unknown time-varying dynamic obstacles in the path planning of ant colony optimization in dynamic environment.
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