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 identification is essential for contact interactive applications of industrial robots. This research aims to solve two problems affecting parameter identification: the incompleteness of payload identification parameter consideration, and the real-time and security of large 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.