Advanced IBVS Algorithm of Robot Arm for Dynamic Tracking in Constrained Environment

It is well known that in space stations, robotic arms are pivotal in on-orbit operations such as maintenance, assembly, and scientific experiments. With the increase in space exploration and utilization, efficiently and reliably managing space debris becomes increasingly critical. Therefore, the Image-based Visual Servoing (IBVS) is of great significance for enabling precise control and manipulation of robotic arms through visual feedback. However, traditional IBVS algorithms encounter significant challenges in tracking space debris and complex environments for robotic arms, such as predicting target motion and handling visual occlusion. To solve the above problem, this paper proposes an advanced IBVS algorithm, which integrates advantages of Model Predictive Control (MPC) and Reinforcement Learning (RL) techniques, leading in the significant efficiency improvement for tracking space debris in constrained environments. The innovations of this work are primarily reflected in the following aspects: First, by controlling the camera's pose changes, the issue of dynamic targets leaving the field of view is addressed. This overcomes the traditional IBVS constraint where the end effector's pose must remain within a specific range to ensure visual feature stability. The enhanced IBVS algorithm integrates to address the complex constraints and uncertainties inherent in tracking space debris. Second, given the limitations of global camera installations and the potential for target occlusion, this study employs an eye-in-hand camera configuration. This setup grants the camera greater freedom of movement, enhancing its ability to maintain target visibility. Additionally, by combining the predictive capabilities of MPC with the adaptive optimization capabilities of RL, the process of visual feature extraction and matching is optimized. This enhances target recognition and tracking capabilities in complex backgrounds. The algorithm also incorporates adaptive control mechanisms to cope with the unpredictability of target motion and environmental changes, thereby improving reliability and efficiency. Experimental results demonstrate that the enhanced IBVS algorithm performs excellently in various dynamic scenarios, achieving highly precise and stable tracking of space debris. In summary, the proposed enhanced IBVS algorithm significantly improves the operational performance of robotic arms in complex space station environments, which demonstrates its potential application in future space exploration missions.