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Updated: May 28, 2026

Robotic Mirror Therapy System for Functional Recovery of Hemiplegic Arms
10:32

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Published on: August 15, 2016

A Reactive Synchronized Motion Controller for Dual-Arm Cooperation with Closed-Chain Constraints.

Fengjia Ju1, Zijian Wang1, Mingda Ge1

  • 1School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China.

Biomimetics (Basel, Switzerland)
|May 26, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel dual-arm synchronized motion controller (SMC) that ensures coordinated movements even with dynamic obstacles. The controller maintains synchronization and enhances robot performance, validated through simulations and experiments.

Keywords:
closed-chain constraintsdual armsquadratic programmingsynchronized motion control

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Area of Science:

  • Robotics
  • Control Systems
  • Artificial Intelligence

Background:

  • Dual-arm manipulation requires satisfying closed-chain constraints for coordinated motion.
  • Existing local planning methods fail to guarantee synchronization for dual arms, especially with dynamic obstacles.

Purpose of the Study:

  • To develop a reactive synchronized motion controller (SMC) for dual-arm manipulation.
  • To ensure synchronized dual-arm control while maintaining performance and avoiding obstacles.

Main Methods:

  • Incorporated closed-chain constraints on dual-arm slack velocities using spherical geometric velocity constraints.
  • Implemented a flexible master-slave arm switching strategy for adaptive control.
  • Developed a controller that addresses dynamic obstacles and joint constraints.

Main Results:

  • Achieved synchronized dual-arm control under closed-chain constraints.
  • Demonstrated enhanced manipulability and effective obstacle avoidance.
  • Validated compliance with joint angle and velocity constraints.

Conclusions:

  • The proposed dual-arm reactive synchronized motion controller (SMC) effectively achieves synchronized motion.
  • The controller preserves excellent motion performance, including obstacle avoidance and constraint compliance.
  • Simulations and experiments confirm the approach's validity on a humanoid robot.