Research Analyzer
← Back ICRA 2026

Whole-Body Balance Control of Wheeled-Bipedal Robots for Perception-Less Terrain Adaptation

Young Hun Lee, Jeongdo Ahn, Dongil Park

PDF

AI summary

Key figure (auto-extracted from paper)
A perception-less whole-body control framework enables wheeled-bipedal robots to navigate stairs, slopes, and recover from pushes using combined LQR and QP optimization.
wheeled-bipedal robot whole-body control perception-less locomotion LQR-QP control balance recovery ZMP planning

Problem

Wheeled-bipedal robots require robust balance control on unstructured terrain, but existing methods often depend on terrain perception or lack real-time constraint handling for dynamic balance.

Approach

The method uses a zero moment point (ZMP) motion planner to generate balance-adaptive trajectories, which feeds into a hybrid controller that combines an LQR for wheel torque regulation with a quadratic programming (QP) solver for whole-body joint torque allocation.

Key results

  • Stable locomotion over 2-step stairs and 10° slopes without terrain sensing
  • Effective balance recovery against external physical disturbances
  • High-rate control updates addressing non-minimum phase wheel dynamics
  • Validated whole-body torque allocation maintaining stable center of mass

Why it matters

Provides a practical, perception-free control strategy for deploying mobile robots in complex, real-world environments.

Abstract

In this paper, we present a whole-body control framework that allows a wheeled-bipedal robot to achieve robust locomotion across diverse environments without relying on terrain perception. The proposed approach consists of a whole-body motion planner and an optimization-based torque computation module. By considering the floating-base dynamics of the robot, the motion planner produces terrain-adaptive behaviors using the zero moment point (ZMP) to preserve balance without prior knowledge of the terrain. In addition, the torque computation module combines a linear quadratic regulator (LQR) with a quadratic programming (QP)-based controller. The LQR computes wheel torques to regulate the body angle while addressing the inherent non-minimum phase characteristics. Using these wheel torques, the QP-based controller allocates optimal joint torques to achieve the desired motion and maintain stable balance. The proposed framework is validated on a wheeled-bipedal robot, demonstrating locomotion over various terrains, including slopes, stairs, as well as robustness against external disturbances.

Index terms

Whole-Body Motion Planning and Control Wheeled Robots Optimization and Optimal Control

Related papers