摘要
针对人工势场移动机器人路径规划算法的实时性差、非全局最优、不能完全解决局部极小和振荡的问题,提出了一种基于改进人工势场回归搜索(改进的APF-RS)算法,该方法可以在完全已知环境中得到全局最优路径而不陷入局部最小或出现振荡。重新定义了势场力函数来消除不可达和局部极小问题,通过设立虚拟目标解决机器人振荡问题,并开发了回归搜索的方法来优化路径。仿真证明该方法的可行性和有效性。
For an improved artificial potential field based regression search method,we obtain a global sub-optimal/optimal path efficiently without local minima and oscillations in complete known environment information. Otential functions is redefined to eliminate non-reachable and local minima problems, and virtual local target is utilized for a robot to escape oscillalions and develop a regression search method to optimize the planned path. The simulations prove that the proposed improved APF-based regression search method is feasible and efficient.
出处
《江南大学学报（自然科学版）》
CAS
2014年第2期167-173,共7页
Joural of Jiangnan University (Natural Science Edition)
关键词
自主式移动机器人
路径规划
人工势场
回归搜索
autonomous mobile robot, path planning, artificial potential field, regression search
