An automatic modeling method for modular reconfigurable robots based on model identification

Modular reconfigurable robots (MRRs) have high reconfigurability and can meet the needs of customized production in modern manufacturing. After each MRR is assembled, the reprogramming process frequently requires time-consuming and expensive professional work. The wide applications of MRRs call for automatic reprogramming methods. In this research, an automatic framework is proposed for solving the MRR modeling problem based on the D–H (Denavit–Hartenberg) convention and dynamic parameter identification. Simulations and experiments are conducted on AUBO I-series modular robots. Three other existing modeling algorithms are adopted to make comparison with the proposed one. It is verified that the proposed framework can achieve the desired automatic modeling process with higher accuracy. Specifically, the accuracy of dynamic parameter identification is enhanced by more than 23% in typical experimental scenarios.