Adaptive visual servoing with an uncalibrated camera using extreme learning machine and Q-leaning

Abstract

In this paper, a novel image-based visual servoing (IBVS) method using Extreme Learning Machine (ELM) and Q-learning is proposed to solve the problems of complex modeling and selection of the servo gain. First, the pseudoinverse of the interaction matrix is approached by ELM which avoids the singularity of the interaction matrix effectively and is robust to interferences such as feature noises and camera calibration errors. Second, a reinforcement learning method, Q-learning, is adopted to adaptively adjust the servo gain in order to improve the convergence speed and stability. Compared with other methods, ELM has better generalization performance, faster operation speed and a unique optimal solution. Also, Q-learning has self-learning ability without experience in advance. The effectiveness of the proposed method is validated by simulations and experiment on a 6-DOF robot with eye-in-hand configuration.