控制理论与应用2012，Vol.29Issue(4)：470-476,7.
基于k-最近邻分类增强学习的除冰机器人抓线控制
Line-grasping control of de-icing robot based on k-nearest neighbor reinforcement learning
摘要
Abstract
The flexible mechanical characteristic of power lines induces difficulties for line-grasping control for de-icing robots.To deal with this difficulty,we propose for de-icing robots a line-grasping control approach which combines the k-nearest neighbor（KNN） algorithm and the reinforcement-learning（RL）.In the learning iteration,the state-perception mechanism of the KNN algorithm selects k-nearest states and weights;from k-weighted states,an optimal action is determined.By expressing a continuous state by k-nearest discrete states in this way,this approach effectively ensures the convergence for the computation and avoids the curse of dimensionality occurred in traditional continuous state-space generalization methods.Abilities of RL in perception and adaptation to the environment make the line-grasping control to tolerate possible errors in robot model,errors of robot arm attitudes and interferences from the environment.The design procedures are presented in details.Simulation results of line-grasping control based on this approach are given.关键词
除冰机器人/k–最近邻分类算法/增强学习/维数灾难Key words
de-icing robot/k-nearest neighbor/reinforcement learning/curse of dimension分类
信息技术与安全科学引用本文复制引用
魏书宁,王耀南,印峰,杨易旻..基于k-最近邻分类增强学习的除冰机器人抓线控制[J].控制理论与应用,2012,29(4):470-476,7.基金项目
国家科技支撑计划资助项目 （）
