林荣霞.基于强化学习的双足机器人的实时避障位置控制[J].电子测量技术,2019,42(10):33-37
基于强化学习的双足机器人的实时避障位置控制
Real-time obstacle avoidance position control for biped robot based on reinforcement learning
  
DOI:
中文关键词:  强化学习  双足机器人  实时避障  位置控制
英文关键词:reinforcement learning  biped robot  real-time obstacle avoidance  position control
基金项目:
作者单位
林荣霞 广东工业大学华立学院 广州 511325 
AuthorInstitution
Lin Rongxia Huali College Guangdong University of Technology, Guangzhou 511325, China 
摘要点击次数: 831
全文下载次数: 536
中文摘要:
      为了提高双足机器人的实时控制能力,提出一种基于强化学习的双足机器人的实时避障位置控制方法。以机器人的双足步行稳定性为控制目标函数,构建双足机器人的实时路径动力学模型,以机器人质心运动的加速度和惯性力矩为被控对象,采用等效碰撞子模型进行双足机器人的实时避障路径规划,采用碰撞子模型和摆动子模型相结合的方法,进行双足机器人行走路径的纠偏参量反馈调节,采用模糊强化学习跟踪方法,进行双足机器人的误差增益控制,实现双足机器人的实时避障位置控制。仿真结果表明,采用该方法进行双足机器人控制的实时避障性能较好,纠偏能力较强,提高了机器人的自适应控制能力。
英文摘要:
      In order to improve the real-time control ability of biped robot, a real-time obstacle avoidance position control method for biped robot based on reinforcement learning is proposed. Taking the stability of biped walking as the control objective function, the real-time path dynamics model of biped robot is constructed. The acceleration and inertia moment of the robot′s centroid motion are taken as the controlled object. The effective collision sub-model is used to plan the real-time obstacle avoidance path of biped robot, and the collision sub-model and swing sub-model are combined to adjust the error correction parameters of biped robot. The fuzzy reinforcement learning tracking method is used to control the error gain of biped robot, and the real time obstacle avoidance position control of biped robot is realized. The simulation results show that the proposed method can avoid obstacles in real time and improve the adaptive control ability of biped robot.
查看全文  查看/发表评论  下载PDF阅读器