KNN – Q ( λ ) algorithm-based line-grasping control of a de-icing robot

Abstract

Flexible mechanical characteristics of power lines and the complex environment induce difficulties for line-grasping control of a de-icing robot when crossing obstacles. An online learning supported line-grasping control method for a de-icing robot based on the KNN–Q(λ) algorithm is presented in this paper. The proposed KNN–Q(λ) algorithm combines reinforcement learning with the k-nearest neighbor algorithm to perceive continuous states and uses a collective decision making mechanism in the action selection process to output continuous actions. Benefiting from the ability of online learning, the KNN–Q(λ)-based line-grasping control method may tolerate possible robot model errors, robot arm attitude errors and environment interferences to line-grasping control. Simulation experiment results show that as a continuous-states-continuous-actions reinforcement learning algorithm, the KNN–Q(λ) algorithm outperforms traditional reinforcement learning algorithms for the application of line-grasping control in terms of leaning rate and robustness.