Image-based visual servoing (IBVS) is a widely-used approach in robotics that employs visual information to guide robots towards desired positions. However, occlusions in this approach can lead to visual servoing failure and degrade the control performance due to the obstructed vision feature points that are essential for providing visual feedback. In this paper, we propose a Control Barrier Function (CBF) based controller that enables occlusion-free IBVS tasks by automatically adjusting the robot's configuration to keep the feature points in the field of view and away from obstacles. In particular, to account for measurement noise of the feature points, we develop the Probabilistic Control Barrier Certificates (PrCBC) using control barrier functions that encode the chance-constrained occlusion avoidance constraints under uncertainty into deterministic admissible control space for the robot, from which the resulting configuration of robot ensures that the feature points stay occlusion free from obstacles with a satisfying predefined probability. By integrating such constraints with a Model Predictive Control (MPC) framework, the sequence of optimized control inputs can be derived to achieve the primary IBVS task while enforcing the occlusion avoidance during robot movements. Simulation results are provided to validate the performance of our proposed method.

翻译：图像视觉伺服（IBVS）是机器人学中一种广泛采用的方法，它利用视觉信息引导机器人到达期望位置。然而，该方法中的遮挡可能导致视觉伺服失败，并因提供视觉反馈所必需的特征点被阻挡而降低控制性能。本文提出一种基于控制屏障函数（CBF）的控制器，通过自动调整机器人构型使特征点保持在视野内并远离障碍物，从而实现无遮挡的IBVS任务。特别地，为考虑特征点的测量噪声，我们利用控制屏障函数构建了概率控制屏障证书（PrCBC），将不确定性下的机会约束遮挡避免条件编码为机器人的确定性可行控制空间，从而确保机器人最终构型能使特征点以满足预定概率的方式保持无遮挡状态。通过将此约束条件与模型预测控制（MPC）框架相结合，可以推导出优化的控制输入序列，在机器人运动过程中强制执行遮挡避免的同时完成主要的IBVS任务。仿真结果验证了所提方法的性能。