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<p>This article presents an algorithm for online estimation of foot position and orientation based on the ground profile, designed to improve humanoid stability during locomotion and stationary poses. The method leverages foot-mounted terrain sensing to determine the optimal foothold and orientation that maximize contact surface on uneven terrain. To address hardware limitations, we also integrate compliant feet that enhance adaptability to rough ground. We evaluated the approach through terrain estimation tests and walking experiments in both simulation and on a real humanoid robot with flat and compliant feet. Results show that the algorithm increases the number of successful steps, improves stability, reduces peak ground reaction forces, and lowers ankle angle variations, by enhancing locomotion safety.</p>

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Enhancing Humanoid Robot Stability on Uneven Terrain with Online Foothold Planning Based on Ground Scanning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月30日

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  • Irene Frizza,
  • Hiroshi Kaminaga,
  • Rafael Cisneros-Limón,
  • Philippe Fraisse,
  • Gentiane Venture

摘要

This article presents an algorithm for online estimation of foot position and orientation based on the ground profile, designed to improve humanoid stability during locomotion and stationary poses. The method leverages foot-mounted terrain sensing to determine the optimal foothold and orientation that maximize contact surface on uneven terrain. To address hardware limitations, we also integrate compliant feet that enhance adaptability to rough ground. We evaluated the approach through terrain estimation tests and walking experiments in both simulation and on a real humanoid robot with flat and compliant feet. Results show that the algorithm increases the number of successful steps, improves stability, reduces peak ground reaction forces, and lowers ankle angle variations, by enhancing locomotion safety.