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A Robust Disturbance Rejection Whole-Body Control Framework for Bipedal Robots Using a Momentum-Based Observer.

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Summary
This summary is machine-generated.

This study introduces a robust planner and controller for bipedal robots, improving gait stability against disturbances. It uses model predictive control (MPC) and a momentum-based observer, eliminating the need for foot sensors to estimate external forces.

Keywords:
biped robotdivergent component of motionmomentum-based observerwhole-body control

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Area of Science:

  • Robotics
  • Control Systems
  • Mechanical Engineering

Background:

  • Bipedal robots require robust control strategies to maintain stability during locomotion.
  • External disturbances pose a significant challenge to bipedal robot performance and safety.
  • Current methods often rely on foot-mounted sensors for disturbance estimation, limiting their applicability.

Purpose of the Study:

  • To develop a comprehensive planner and controller scheme for bipedal robots.
  • To enhance robustness against external disturbances without relying on foot-mounted sensors.
  • To improve the accuracy of ground reaction force estimation and disturbance compensation.

Main Methods:

  • High-level planning using Model Predictive Control (MPC) optimized foothold location and step duration based on the divergent component of motion (DCM).
  • Low-level control employing a momentum-based observer to estimate external forces on stance and swing legs.
  • Integration of full-body dynamics and estimated disturbances into Weighted Whole-Body Control (WBC) for accurate ground reaction force calculation.

Main Results:

  • Demonstrated enhanced gait robustness against external disturbances.
  • Successfully estimated external forces without foot-mounted sensors.
  • Validated the controller's effectiveness in mitigating disturbance effects through simulations and experiments on the BRUCE robot.

Conclusions:

  • The proposed planner and controller scheme significantly improves bipedal robot robustness against external disturbances.
  • The method offers a sensor-independent approach to disturbance estimation and compensation.
  • Experimental validation confirms the framework's effectiveness in diverse challenging scenarios.