基于改进ACS算法的移动机器人路径规划研究

针对蚁群系统(Ant Colony System,ACS)算法存在收敛速度慢、路径不平滑、易陷入局部最优等缺点,提出了一种基于万有引力搜索策略的ACS算法.为了解决算法初期由于地图信息匮乏,导致蚁群寻路盲目性较大的问题,提出了简化ACS算法对初始信息素浓度进行更新.引入万有引力算法搜索策略,提升了算法收敛速度,且有效解决了局部最优问题.对每次迭代获取到的最优路径进行优化,减少了路径的转折点数量、提升了路径平滑性.仿真试验表明,改进算法能够有效提升算法的收敛速度、路径平滑性.将改进算法应用到实际的移动机器人导航试验中,试验结果表明,改进算法能够有效解决移动机器人的路径规划问题,且有效提升移动机器人的导航效率.

Research on Path Planning of Mobile Robot Based on Improved ACS Algorithm

Aiming at the shortcomings of slow convergence speed, unsmooth path and easy to fall into local optimum in Ant Colony System(ACS) algorithm, an ACS algorithm based on gravitational search strategy is proposed. Firstly, in order to solve the problem that the lack of map information in the initial stage of the algorithm leads to the great blindness problem of ant colony algorithm, a simplified ant colony algorithm is proposed to update the initial pheromone concentration; secondly, the search strategy of gravity algorithm is introduced to improve the speed of the later algorithm and effectively solve the local optimal problem; finally, the optimal path obtained by each iteration is optimized, which reduces the number of turning points and improves the smoothness of the path. Simulation results show that the improved algorithm can effectively improve the convergence speed and path smoothness of the algorithm. Additionally, the improved algorithm is applied to the actual mobile robot navigation experiment. The experimental results show that the improved algorithm can effectively solve the path planning problem of mobile robot, and effectively improve the efficiency of robot navigation.