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Humanoid Robot Cooperative Motion Control Based on Optimal Parameterization.

Qiubo Zhong1,2, Yaoyun Li1, Caiming Zheng1

  • 1Robotics Institute, Ningbo University of Technology, Ningbo, China.

Frontiers in Neurorobotics
|July 8, 2021
PubMed
Summary
This summary is machine-generated.

This study optimizes energy consumption for two humanoid robots performing cooperative movements using an optimal parameter control method. Simulations and experiments confirm the effectiveness of this approach for stable, efficient robot collaboration.

Keywords:
collaborative controlenergy optimizationhumanoid robotparameterized optimization controlstability constraints

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Area of Science:

  • Robotics
  • Control Engineering
  • Artificial Intelligence

Background:

  • Low-energy cooperative movements are crucial for complex humanoid robot control.
  • Optimizing energy consumption in multi-robot systems is an ongoing challenge.
  • Efficient manipulation requires precise trajectory planning and stable control.

Purpose of the Study:

  • To develop and validate an optimal parameter control method for reducing energy consumption in cooperative humanoid robot movements.
  • To establish a dynamic model for simulating cooperative object manipulation.
  • To ensure stability during coordinated robot motion.

Main Methods:

  • Established a dynamic model for two-robot cooperative manipulation.
  • Planned motion trajectories for cooperative object handling.
  • Implemented an optimal parameter control method to optimize energy consumption.
  • Verified stability using a dedicated index.

Main Results:

  • Successfully optimized energy consumption for cooperative movements.
  • Achieved stable robot motion during manipulation tasks.
  • Validated the control method through simulations and experimental tests.

Conclusions:

  • The proposed optimal parameter control method effectively reduces energy consumption in cooperative humanoid robot tasks.
  • The approach ensures stable and efficient collaborative manipulation.
  • This research contributes to the advancement of energy-efficient humanoid robot control.