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Uneven Terrain Walking with Linear and Angular Momentum Allocation.

Zhicheng He1, Songhao Piao1, Xiaokun Leng1

  • 1School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.

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|February 28, 2023
PubMed
Summary
This summary is machine-generated.

Child-size humanoid robots can now walk on uneven terrain using a novel control framework. This method, based on centroidal momentum allocation, reduces the need for precise ground detection, making robots more adaptable and stable.

Keywords:
centroidal momentummomentum allocationmotion controluneven terrain walking

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Area of Science:

  • Robotics
  • Control Systems
  • Humanoid Robots

Background:

  • Child-size humanoid robots struggle with uneven terrain due to limitations in ground condition detection.
  • Existing control methods often require accurate ground flatness information, which is difficult to obtain.

Purpose of the Study:

  • To develop a walking control framework for child-size humanoid robots capable of navigating uneven terrain without relying on ground flatness detection.
  • To enhance robot stability and adaptability to unknown environments.

Main Methods:

  • A control framework integrating a momentum decreasing controller, posture controller, and admittance controller.
  • Utilizing centroidal momentum allocation for robust locomotion.
  • Employing low-cost sensors: MEMS-based inertial measurement unit (IMU), joint position encoders, and force-sensitive resistors (FSR).

Main Results:

  • The proposed framework enables a child-size humanoid robot to walk on structured non-flat and soft uneven terrain.
  • Demonstrated effectiveness in standing posture balancing and adapting to unknown terrain.
  • Achieved a walking speed of 2.8 seconds per step.

Conclusions:

  • Centroidal momentum allocation is an effective method for enabling child-size humanoid robots to walk on uneven terrain.
  • The developed control framework enhances robot adaptability and stability using affordable components.
  • The system successfully reduces the demand for accurate ground detection, broadening the applicability of humanoid robots.