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Compliance control based on PSO algorithm to improve the feeling during physical human-robot interaction.

Zhongliang Jiang1, Yu Sun1, Peng Gao2

  • 1Mechanical & Electrical Department, Harbin Institutes of Technology Shenzhen Graduate School, Shenzhen, China ; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China ; The Chinese University of Hong Kong, Hong Kong, China.

Robotics and Biomimetics
|December 10, 2016
PubMed
Summary

This study introduces a novel controller for robots that mimics human-like interaction by sensing and responding to physical forces. Experiments on a robotic surgery system demonstrate improved human-robot interaction performance.

Keywords:
Compliance controlHuman–robot interactionParticle swarm optimizationSurgical robot

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Area of Science:

  • Robotics
  • Human-Robot Interaction
  • Control Systems Engineering

Background:

  • Human-robot interaction (HRI) presents challenges compared to human-human interaction.
  • Developing human-like robots is crucial for seamless integration into daily life and collaborative tasks.
  • Existing robotic systems often lack the nuanced responsiveness required for intuitive interaction.

Purpose of the Study:

  • To design and implement a novel controller for robots that enhances human-robot interaction.
  • To enable robots to sense and react to forces applied at any point, promoting more natural movements.
  • To improve the human-like qualities of robots through advanced control mechanisms.

Main Methods:

  • A spring-mass-dashpot physical model was employed to describe the robot's dynamics.
  • A second-order system formed the core of the controller, with state-space equations established.
  • Particle swarm optimization (PSO) was utilized for system parameter tuning.
  • Root-locus analysis was performed to assess system stability.

Main Results:

  • The developed controller successfully enabled robots to move in response to sensed forces.
  • Stability analysis using root-locus diagrams confirmed the system's robustness.
  • Experimental validation on a robotic spinal surgery system showed superior performance in HRI scenarios.
  • The controller facilitated more intuitive and safer interactions during surgical procedures.

Conclusions:

  • The novel force-sensing controller significantly improves human-robot interaction.
  • The system's design, incorporating PSO and state-space modeling, offers a robust solution for human-like robotics.
  • This advancement holds promise for more integrated and effective human-robot collaboration in various fields, including surgery.