Rich and robust bio-inspired locomotion control for humanoid robots
(2017)
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- Abstract
- Bipedal locomotion is a challenging task in the sense that it requires to maintain dynamic balance while steering the gait in potentially complex environments. In this thesis, we take inspiration from the impressive human walking capabilities to design neuromuscular controllers for humanoid robots. More precisely, we control the robot motors to reproduce the action of virtual muscles commanded by neural signals, similarly to what is done during human locomotion. Gait richness and robustness are two key aspects of this work. In other words, the gaits developed in this work can be steered by an external operator, while being resistant to external perturbations. In the beginning of this thesis, we adapt and port an existing reflex-based neuromuscular model from a 2D simulation environment to a real robotic platform. Starting from this model, we progressively iterate and update the neural commands to add new features. The 2D walker controllers are first incremented to generate walking gaits across a range of forward speeds close to the normal human one. By using a similar control method, we also obtain 2D running gaits whose speed can be controlled by a human operator. The walking controllers are later extended to 3D scenarios (i.e. no motion constraint) with the capability to adapt both the forward speed and the heading direction (including curvature). In parallel, we also develop a method to automatically learn neural networks for a given task and we study how flexible feet affect the gait in terms of robustness and energy efficiency.
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Citations
Van der Noot, N. (2017). Rich and robust bio-inspired locomotion control for humanoid robots. https://hdl.handle.net/2078.5/62105