Research on Human-Robot Collaboration Method for Parallel Robots Oriented to Segment Docking.

Journal: Sensors (Basel, Switzerland)
PMID:

Abstract

In the field of aerospace, large and heavy cabin segments present a significant challenge in assembling space engines. The substantial inertial force of cabin segments' mass often leads to unexpected motion during docking, resulting in segment collisions, making it challenging to ensure the accuracy and quality of engine segment docking. While traditional manual docking leverages workers' expertise, the intensity of the labor and low productivity are impractical for real-world applications. Human-robot collaboration can effectively integrate the advantages of humans and robots. Parallel robots, known for their high precision and load-bearing capacity, are extensively used in precision assembly under heavy load conditions. Therefore, human-parallel-robot collaboration is an excellent solution for such problems. In this paper, a framework is proposed that is easy to realize in production, using human-parallel-robot collaboration technology for cabin segment docking. A fractional-order variable damping admittance control and an inverse dynamics robust controller are proposed to enhance the robot's compliance, responsiveness, and trajectory tracking accuracy during collaborative assembly. This allows operators to dynamically adjust the robot's motion in real-time, counterbalancing inertial forces and preventing collisions between segments. Segment docking assembly experiments are performed using the Stewart platform in this study. The results show that the proposed method allows the robot to swiftly respond to interaction forces, maintaining compliance and stable motion accuracy even under unknown interaction forces.

Authors

  • Deyuan Sun
    School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China.
  • Junyi Wang
  • Zhigang Xu
    School of Materials and Energy and Chongqing Engineering Research Center for Micro-Nano Biomedical Materials and Devices, Southwest University, Chongqing, 400715, China.
  • Jianwen Bao
    Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.
  • Han Lu
    Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, 55414, USA.