Autonomous obstacle crossing control of wall climbing robot based on fuzzy iterative Q-learning

Abstract

In order to overcome the problems of low robot positioning accuracy, low success rate of autonomous obstacle crossing, and long response time in traditional methods, an autonomous obstacle crossing control method of wall climbing robot based on fuzzy iterative Q-learning is proposed. Build a dynamic model of the wall climbing robot based on the forces acting on its wall movement, and use the current pose in the dynamic model as the system state vector to locate the wall climbing robot using particle filtering. Based on the results of the wall climbing robot and the Q-learning algorithm, a fuzzy iterative Q-learning controller is constructed to achieve autonomous obstacle crossing control of the wall climbing robot. Experimental results show that the maximum absolute error of the proposed method for wall climbing robot positioning is 1.9 mm, the maximum success rate of autonomous obstacle crossing is 98.4%, and the minimum response time is 0.85 s.