A Reactive Walking Pattern Generator Based on Nonlinear Model Predictive Control

Maximilien Naveau (1) , Manuel Kudruss (2) , Olivier Stasse (1) , Christian Kirches (2) , Katja Mombaur (2) , Philippe Souères (1)

1 LAAS-GEPETTO - Équipe Mouvement des Systèmes Anthropomorphes
2 IWR - Interdisciplinary Center for Scientific Computing

The contribution of this work is to show that real-time nonlinear model predictive control (NMPC) can be implemented on position controlled humanoid robots. Following the idea of " walking without thinking " , we propose a walking pattern generator that takes into account simultaneously the position and orientation of the feet. A requirement for an application in real-world scenarios is the avoidance of obstacles. Therefore the paper shows an extension of the pattern generator that directly considers the avoidance of convex obstacles. The algorithm uses the whole-body dynamics to correct the center of mass trajectory of the underlying simplified model. The pattern generator runs in real-time on the embedded hardware of the humanoid robot HRP-2 and experiments demonstrate the increase in performance with the correction.

Mots clés: Humanoid and Bipedal Locomotion; Humanoid Robots; Nonlinear Model Predictive Control

Domaines: Robotique [cs.RO]

Maximilien Naveau, Manuel Kudruss, Olivier Stasse, Christian Kirches, Katja Mombaur, et al.. A Reactive Walking Pattern Generator Based on Nonlinear Model Predictive Control. IEEE Robotics and Automation Letters, 2017, 2 (1), pp.10-17. ⟨10.1109/LRA.2016.2518739⟩. ⟨hal-01261415⟩