Deep Reinforcement Learning-Based Uncalibrated Visual Servoing Control of Manipulators with FOV Constraints

In this article, we put forward a brand-new uncalibrated image-based visual servoing (IBVS) method. It is designed for monocular hand–eye manipulators with Field-of-View (FOV) feature constraints and makes use of a deep reinforcement learning (DRL) approach. First, the IBVS and its feature-loss problems are introduced. Then, a uncalibrated IBVS method is presented to address the feature-loss issue and improve servo efficiency with DRL. Specifically, the uncalibrated IBVS is integrated into the deep Q-network (DQN) control framework to ensure analytical stability. Additionally, a feature-constrained Q-network based on offline camera FOV environment feature mapping is designed and trained to adaptively output compensation for the IBVS controller, which helps maintain the feature within the camera’s FOV and improve servo performance. Finally, to further demonstrate the effectiveness and practicality of the proposed DQN-based uncalibrated IBVS method, experiments are conducted on a 6-DOF manipulator, and the results validate the proposed approach.