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.

中文翻译：

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使用极限学习机和 Q 学习的未校准相机的自适应视觉服务

摘要 在本文中，提出了一种使用极限学习机（ELM）和Q-learning的基于图像的视觉伺服（IBVS）方法来解决伺服增益的复杂建模和选择问题。首先，通过 ELM 逼近交互矩阵的伪逆，有效地避免了交互矩阵的奇异性，并且对诸如特征噪声和相机校准误差等干扰具有鲁棒性。其次，采用强化学习方法Q-learning自适应调整伺服增益，以提高收敛速度和稳定性。与其他方法相比，ELM 具有更好的泛化性能、更快的运算速度和独特的最优解。此外，Q-learning 具有无需事先经验的自学习能力。