A Two-Stage Payload Dynamic Parameter Identification Method for Interactive Industrial Robots with Large Components

Taking human-robot collaborative assembly as an example, the methods based on contact forces can improve the assembly efficiency of industrial robots with large components in industrial manufacturing. However, due to the large size, high payload, assembly accuracy and dynamic changes in grip position, accurately estimating the contact forces between the payload and the operator becomes challenging when handling these large components. In this paper, a two-stage method is proposed for payload dynamic parameter identification. The parameter identification equation in the sensor coordinate system is initially established. Furthermore, the identification model of recursive restricted total least squares (RRTLS) based on total least squares (TLS) is constructed to achieve low-consumption online identification. According to the assembly requirements and payload characteristics, the posture coordinate system is designed for safety, including the feasible workspace for the robot. Subsequently, the static identification postures and dynamic excitation trajectory are planned to obtain static values and dynamic inertial parameters. In the end, a high-payload human- robot collaborative assembly system is built to validate the proposed method. Experimental results show that compared with the existing methods, the proposed approach can effectively identify and compensate the payload, leading to more accurate external force sensing. Note to Practitioners—Accurate payload parameter identifica- tion is essential for contact interactive applications of industrial robots. This research aims to solve two problems affecting param- eter identification: the incompleteness of payload identification parameter consideration, and the real-time and security of large Received 29 August 2024; revised 21 January 2025; accepted 22 March 2025. Date of publication 2 April 2025; date of current version 24 April 2025. This article was recommended for publication by Associate Editor B. Lacevic and Editor P. Rocco upon evaluation of the reviewers’ comments. This work was supported in part by the National Natural Science Foundation of China under Grant 52205530, Grant 52205017, and Grant U22A20204; and in part by the Civil Aerospace Technology Advance Research Project of the National Defense Administration of Science and Technology under Grant D020201. (Corresponding authors: Pengcheng Li; Wei Tian.) Mingxuan Liu is with the College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China (e-mail: letmexx@nuaa.edu.cn). Pengcheng Li, Jinjun Duan, and Wei Tian are with the College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China, and also with the State Key Laboratory of Intelligent Manufacturing System Technology for Complex Products, Beijing 100854, China (e-mail: lpccmee@nuaa.edu.cn; tw nj@nuaa.edu.cn). Lunqian Liu and Yuqi Ji are with Shanghai Aircraft Manufacturing Com- pany, Shanghai 200120, China. Ye Shen is with Shanghai Aerospace Electronic Technology Research Institute, Shanghai 201109, China. This article has supplementary downloadable material available at https://doi.org/10.1109/TASE.2025.3557064, provided by the authors. Digital Object Identifier 10.1109/TASE.2025.3557064 payload identification in industrial scenarios. In this paper, a two-stage payload dynamic parameter identification method is proposed, which can meet the application of large components in industrial scenarios. Firstly, a complete parameter identification model is established by decoupling the dynamic and static parameters. Then, a low-cost recursive identification is realized in a set feasible workspace. Finally, the external force sensing after parameter identification shows potential in but not limited to human-robot collaboration applications.