A smooth path planning method for mobile robot with A* ant colony optimization was proposed based on dynamic feedback for mobile robot.First of all,in order to overcome the disadvantage about slow convergence speed of ant colony algorithm,simplified A* algorithm was presented to optimize the initial pheromone settings,which was able to solve the blindness of the first search.In this step,the planning path with the minimum value of the valuation function was obtained by the evaluation function of A* algorithm.And the presented multi-evolutionary strategy mechanism which could increase search space was used to strengthen the global search ability of the algorithm.Secondly,in order to further improve the adaptability of algorithm about the problem of local minimum and stagnation in the path planning,the key parameters of the algorithm were systematically analyzed and the closed-loop feedback idea was adopted to adjust the parameters of ant colony optimization algorithm dynamically.Finally,combining with the Cubic B spline curve method,the planning path was smoothed to meet the practical movement route of mobile robot.The simulation experiment results showed that compared with traditional ant colony (AC),A* ant colony optimization based on dynamic feedback could reduce 10.4％ of the average path cost and shorten 65.8 ％ of the computing time in average.In addition,compared with ant colony system (ACS),the average path cost could be reduced by 5.9％,the calculation time could be shortened by 52.6％.The improved ant colony optimization algorithm could plan a smooth and high quality path in both the dynamic and static environments.%针对移动机器人提出了一种基于动态反馈A*蚁群算法的平滑路径规划方法.首先,为了克服蚁群算法收敛速度慢的缺点,提出了简化A*算法来优化初始信息素设置以解决初次搜索的盲目性,并借鉴多策略进化机制加强算法的全局搜索能力.其次,为了进一步提高算法在路径规划中的适应能力,解决陷入局部极小和停滞问题,引入闭环反馈思想来实现参数的动态自适应调节.最后,结合三次B样条曲线对所规划的路径进行平滑处理,以满足移动机器人实际运动路径的要求.通过仿真表明:与原蚁群算法相比,动态反馈A *蚁群算法平均可减少10.4％的路径成本和65.8％的计算时长.同时,该算法在动态和静态环境中,均能快速规划出一条光滑优质路径.
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