A Motion Control Approach for Physical Human-Robot-Environment Interaction via Operational Behaviors Inference

• Published in: IEEE transactions on industrial electronics (1982) 2024-09, p.1-11
• Main Authors: Lang, Yilin, Li, Zihao, Li, Zhaoyang, Li, Yanan, Ren, Qinyuan
• Format: Article
• Language: English
• Subjects: Behavioral sciences; Force; Human–robot-environment interaction (HREI) control; Long short term memory; long short-term memory (LSTM); Manipulator dynamics; Optimization; Predictive models; Robots; trajectory optimization (TO)
• ISSN: 0278-0046; 1557-9948
• DOI: 10.1109/TIE.2024.3447741

Human-robot collaboration systems aim to improve working efficiency and reduce human workload. However, inefficient assimilation of human potential behaviors often leads to increasing human-robot conflicts. In this article, a motion optimization approach integrating behavior inference is presented for physical human-robot-environment interaction (pHREI) tasks to implement assistive behavior. A multistep human behaviors model with long short-term memory (LSTM) mechanism is employed, enabling continuous prediction of the human intention based on robot and environment states. A two-layer control scheme is developed to optimize the manipulator trajectory with the objective of reducing human workload in a model predictive control (MPC) fashion. A series of experiments is conducted to verify the proposed scheme on a wood-sawing task. With the integration of the human and environment model, the proposed control scheme significantly reduces the human workload while eliminating the human-robot conflict simultaneously.