论文部分内容阅读
采用微粒群优化解决机器人全局路径规划问题,近年来得到国内外学者广泛关注,并已经取得丰硕的研究成果。但是,已有成果往往难以应用于含有密集障碍物的环境。针对解决含有密集障碍物环境的机器人全局路径规划问题,提出一种双层微粒群优化方法。该方法通过底层微粒群优化,得到若干最优路径;通过顶层微粒群优化,在这些最优路径附近局部搜索,从而得到机器人的全局最优路径;通过对不可行路径实施脱障操作,使其成为可行路径。将所提方法应用于多场景的机器人路径规划,并与已有方法进行比较。实验结果表明,该方法能够找到机器人的全局最优路径。
Particle swarm optimization is used to solve the problem of global path planning of robots. In recent years, it has received wide attention from domestic and foreign scholars and has achieved fruitful research results. However, the achievements that have been made are often difficult to apply to environments containing dense obstacles. In order to solve the global path planning problem of robots with dense obstacles, a two-tier particle swarm optimization method is proposed. In this method, a number of optimal paths are obtained through the optimization of the underlying particle swarm optimization. By top-level particle swarm optimization, local search is performed in the vicinity of these optimal paths to obtain the global optimal path of the robot. By performing a fault-free operation on infeasible paths, Become a viable path. The proposed method is applied to multi-scenario robot path planning and compared with the existing methods. Experimental results show that this method can find the global optimal path of the robot.