摘要
为了解决人工势场法的“最小值陷阱”问题以及传统路径规划算法在动态障碍物环境下的局限性,提出基于改进的人工势场法路径规划控制算法方案。在可调半径R的虚拟势场检测圆模型基础上,提前检测由障碍物斥力场形成的“最小值陷阱”,并建立无人车运动模型,结合基于LSTM改进的强化学习算法调节虚拟势场检测圆半径R来实现针对动态障碍物的有效规避,实现了无人车在半封闭的动态障碍物环境下在线无碰撞路径的规划。该算法的有效性和鲁棒性通过Python仿真实验得到了验证。
In order to solve the "minimum trap" of artificial potential field method and the limitation of traditional path planning algorithm in dynamic obstacle environment,a path planning control algorithm based on improved artificial potential field method is proposed. A virtual potential field detection circle model with adjustable radius R is proposed to detect the "minimum trap" formed by the repulsion field of obstacles in advance,and the motion model of unmanned vehicle is established. Combined with the improved reinforcement learning algorithm based on LSTM,the radius R of virtual potential field detection circle is adjusted to achieve effective avoidance of dynamic obstacles. The online collision free path planning of unmanned vehicle in semi closed dynamic obstacle environment is realized. The effectiveness and robustness of the algorithm are verified by Python simulation.
作者
罗洁
王中训
潘康路
卢中原
刘言
LUO Jie;WANG Zhongxun;PAN Kanglu;LU Zhongyuan;LIU Yan(School of Opto-Electronic Information,Yantai University,Yantai 264005,China)
出处
《电子设计工程》
2022年第17期90-94,99,共6页
Electronic Design Engineering
关键词
人工势场法
虚拟势场检测圆模型
LSTM
路径规划
强化学习
artificial potential field method
virtual potential field detection circle model
LSTM
path planning
reinforcement learning
