摘要
现有的大多数动态RRT路径规划算法不能使规划的路径远离障碍物,这有可能导致机器人没有足够的避障时间。针对此问题,提出了一种利用人工势场引导快速扩展随机树向目标区域生长并远离障碍物的改进RRT算法APFG-RRT(artificial potential field guided RRT)。为了进一步加快算法的收敛速度、加速算法跳出局部极小值,引入了一种按自适应概率选择目标点作为采样点的策略;针对动态环境采用全局规划结合局部重新规划的方法以提高算法的实时性。仿真实验表明,相比于初始RRT和Goal-bias RRT,APFG-RRT的计算效率更高,内存需求更小,并且搜索到的路径能够有效地远离障碍物,提高了动态路径规划的成功率。
Most of the existing dynamic RRT path planning algorithms cannot keep the planned path away from obstacles,which may cause the robot to have insufficient time to avoid obstacles.To solve this problem,this paper proposed an improved RRT,denoted as APFG-RRT,which utilized artificial potential fields to guide the RRT grow to goal and away from obstacles.In order to further increase the convergence rate and speed up the jump out of local minima,it introduced a strategy of selecting the goal as the random sample at an adaptive probability.Finally,it adopted global planning combined with local replanning to improve its real-time performance in dynamic environment.Simulation experiments show that APFG-RRT has higher computational efficiency and lower memory requirements compared with the initial RRT and Goal-bias RRT,and the given path can be effectively away from obstacles,which improves the success rate of dynamic path planning.
作者
司徒华杰
雷海波
庄春刚
Situ Huajie;Lei Haibo;Zhuang Chungang(School of Mechanical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)
出处
《计算机应用研究》
CSCD
北大核心
2021年第3期714-717,724,共5页
Application Research of Computers
基金
国家自然科学基金资助项目(51775344)。
关键词
路径规划
RRT
人工势场
动态环境
局部重新规划
path planning
RRT
artificial potential fields
dynamic environments
local replanning
