Applying Q-Learning Algorithm to Study Line-Grasping Control Policy for Transmission Line Deicing Robot

Ice coating in power networks could result in power-tower collapse and power interruption. This paper introduces a preliminary design of deicing robot, which travels on transmission lines and automatically remove ices. Inevitably, the deicing robot will encounter some obstacles. To cross an obstacle, the deicing robot needs to control its arms to grasp transmission line over the obstacle. In this paper, Q-learning, one of reinforcement learning algorithms is applied to study the line-grasping control strategies for deicing robot. We implement a graphical simulation environment and use it to evaluate the Q-learning based line-grasping control policy study algorithm. Simulation results show that the proposed algorithm is promising for the line-grasping control of deicing robot.