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Walking Pattern Generation Through Step-by-Step Quadratic Programming for Biped Robots.

Guoshuai Liu1, Zhiguo Lu1, Hang Zhang1

  • 1School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China.

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Summary
This summary is machine-generated.

This study introduces a new method for biped robot walking patterns by integrating support phases. This approach simplifies trajectory generation for more natural and stable robot locomotion.

Keywords:
biped robotheight variationquadratic optimizationwalking pattern generation

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Area of Science:

  • Robotics
  • Control Systems
  • Mechatronics

Background:

  • Biped robot control is complex due to unstable dynamics.
  • Generating stable walking trajectories is crucial for biped robots.

Purpose of the Study:

  • To propose a novel method for generating biped robot walking patterns.
  • To simplify trajectory generation by integrating double and single support phases.
  • To enable natural walking trajectories with a shortened optimization horizon.

Main Methods:

  • Integrating double and single support phases into a single step for trajectory generation.
  • Utilizing quadratic optimization with terminal constraints based on the Linear Inverted Pendulum Model.
  • Constraining the center of mass (COM) position and acceleration vertically to manage Zero Moment Point (ZMP) nonlinearities.

Main Results:

  • Demonstrated shortened optimization horizons for natural walking trajectory generation.
  • Successfully offset nonlinear effects of COM vertical motion on ZMP.
  • Validated the method through simulations and experiments on flat ground and stairs with the Neubot robot.

Conclusions:

  • The proposed method effectively generates natural walking trajectories for biped robots.
  • The technique allows for vertical center of mass adjustments while maintaining optimization linearity.
  • The approach is validated for diverse walking scenarios, including stairs.