A fast dynamic pose estimation method for vision-based trajectory tracking control of industrial robots

• Published in: Measurement : journal of the International Measurement Confederation 2024-05, Vol.231, p.114506, Article 114506
• Main Authors: Wei, Xuewen, Li, Pengcheng, Tian, Wei, Wei, Delan, Zhang, Haosong, Liao, Wenhe, Cao, Yansheng
• Format: Article
• Language: English
• Subjects: Industrial robot; Kalman filter; Trajectory tracking control; Visual servoing
• ISSN: 0263-2241
• DOI: 10.1016/j.measurement.2024.114506

[Display omitted] •This paper proposes a fast dynamic pose estimation method for industrial robots.•The encoder-driven error-state kinematics for industrial robots is reconstructed.•This method has robust estimation performance in complex desired trajectories.•This method reduces estimation error by about 50% compared to existing methods.•This method significantly improves the trajectory tracking accuracy of robots. Vision-based robotic trajectory tracking control is considered a promising technology. However, the slow sampling rate and latency of the vision sensor enormously limit the tracking performance. To conquer the issues, this paper proposes a dual-space error-state Kalman filter (DS-ESKF). By combining the encoder measurement with the vision measurement, the end-effector’s pose between adjacent vision measurements is restored, and the pose estimation cycle is synchronized with the control cycle. The critical distinguishing of DS-ESKF is that the encoder-driven error-state kinematics for the industrial robot is reconstructed. The experimental results on the Staubli TX60 industrial robot show that compared with the existing dual-rate Kalman filters (DR-KF), DS-ESKF can reduce estimation errors by about 50 % and have robust estimation performance. By applying the trajectory tracking control scheme combining DS-ESKF with a simple PID controller to Staubli TX60, the tracking accuracy is significantly improved (±0.11 mm for position and ± 0.05°for orientation).