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Biped Walking Based on Stiffness Optimization and Hierarchical Quadratic Programming.

Xuanyang Shi1,2, Junyao Gao1,2, Yizhou Lu1,2

  • 1School of Mechatronical Engineering, Intelligent Robotics Institute, Beijing Institute of Technology, Beijing 100081, China.

Sensors (Basel, Switzerland)
|April 3, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a real-time control method for biped robots, optimizing spring stiffness for stable walking on uneven terrain and disturbance recovery. The approach enhances humanoid robot motion planning.

Keywords:
hierarchical quadratic optimizationspring-loaded inverted pendulum (SLIP)stiffness optimizationuneven ground walking

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Area of Science:

  • Robotics
  • Control Systems
  • Biomechanics

Background:

  • The spring-loaded inverted pendulum model is a standard for humanoid robot motion planning due to similarities with human walking.
  • Existing control methods like velocity feedback and nonlinear optimization have limitations in accuracy, complexity, and real-time application.

Purpose of the Study:

  • To develop a real-time optimization method for humanoid robot motion control.
  • To improve the stability and disturbance rejection capabilities of biped robots on uneven terrain.

Main Methods:

  • Utilized a center of mass (CoM)-velocity feedback controller to determine a virtual landing point.
  • Constructed a touchdown return map and employed nonlinear least squares for spring stiffness optimization.
  • Implemented hierarchical quadratic programming for whole-body control with prioritized dynamic equations.

Main Results:

  • The proposed method enables stable walking on unknown uneven ground with obstacles up to 5 cm.
  • The force-controlled biped robot successfully recovered from a 5 Nm disturbance without falling.
  • Reduced the number of optimizations by directly using inverse dynamics for the highest priority dynamic equation.

Conclusions:

  • The developed CoM-velocity feedback and nonlinear least squares optimization method allows for real-time control of biped robots.
  • The hierarchical quadratic programming approach ensures efficient whole-body control with task prioritization.
  • The system demonstrates robust performance in challenging walking scenarios, including uneven terrain and external disturbances.