Robust Photogrammetry Sensor-Based Real-Time Pose Control of Industrial Robots

This article proposes a novel robust approach for accurate real-time pose control (RPC) of industrial robots based on photogrammetry sensors. The proposed method comprises two main parts: accurate pose detection using a photogrammetry sensor and robust Kalman filter (RKF) and RPC of the robot's end-effector utilizing a chattering-reduced sliding mode controller (CRSMC) with a nonlinear sliding surface. An eye-to-hand photogrammetry sensor (C-Track ATEMEK) detects the robot's pose, and then RKF filters out the noises in the detected pose signals. These filtered signals are fed to the CRSMC, which exploits a fast-nonlinear reaching law and nonlinear sliding surface to provide robustness against existing uncertainties, high tracking accuracy, and fast convergence speed. The stability of the CRSMC in the discrete-time domain is proved, and its performance is analyzed. The simulation results demonstrate the superiority of the proposed approach to other methods in terms of convergence speed, control effort, chattering level, and tracking accuracy. Experimental results on a PUMA 200 robot also show an unprecedented tracking accuracy, i.e., ±0.07 mm and ±0.17° for position and orientation, respectively.