A Bio-inspired Solution for Double Support Force Distribution in Humanoid Robot Locomotion

• Published in: 2023 IEEE-RAS 22nd International Conference on Humanoid Robots (Humanoids) pp. 1 - 8
• Main Authors: Andrade Chavez, Francisco Javier, Rajendran, Vidyasagar, Mombaur, Katja
• Format: Conference Proceeding
• Language: English
• Published: IEEE 12-12-2023
• Subjects: Correlation; Estimation; Footwear; Force; Friction; Humanoid robots; Legged locomotion
• ISSN: 2164-0580
• DOI: 10.1109/Humanoids57100.2023.10375197

In locomotion, the likelihood of slipping or maintaining contact is determined by the forces applied in the environment. Therefore, it is crucial to find methods of keeping forces within friction constraints. In single support, the relationship between center of mass acceleration and forces is unique. However, in double support, it becomes a non-deterministic problem. It is often assumed that forces are distributed to minimize a certain effort criterion. An interesting alternative is to distribute the forces in a manner similar to how a human would, which could result in a more human-like gait for humanoid robots. With this in mind, we introduce the modified Twin Polynomial Method (mTPM) as a technique to accurately distribute double support forces in a human-like way. The relevant relationships used in this method are presented, and we compare its performance against the current state of the art in human gait analysis. The comparison is conducted across three different types of contacts with the environment: barefoot, socks, and shoes. We show how human data differs from a 'typical' robot walk and use the proposed method to generate a more human-like distribution for robots.