A Robust Visual Servoing Controller for Anthropomorphic Manipulators With Field-of-View Constraints and Swivel-Angle Motion: Overcoming System Uncertainty and Improving Control Performance

Abstract

Human–robot collaboration has attracted significant attention in the industry due to the flexibility of humans and the accuracy of robots. Humanoid control of anthropomorphic robotic arms combined with visual servoing will enhance the intelligence of industrial robots. However, the robotic manipulator will introduce psychological discomfort to nearby humans, and the loss of visual features will induce visual servoing task failure. Aiming at these problems, this article proposes a humanoid control method based on visual servoing by utilizing the swivel angle derived from the human arm to realize the human-like behavior of anthropomorphic robot manipulators. To advance the visual servoing control performance, a function constraint is designed with the barrier Lyapunov function (BLF) to ensure that image features stay within the field of view (FoV). The sliding mode control (SMC) is combined with image-based visual servoing (IBVS) to dispose of the uncertainties of a seven-degree-of-freedom (7-DoF) redundant robot manipulator. The proposed algorithm is substantiated through comparison experiments based on the Sawyer robot and constructed visual servoing physical platform.