000230249 001__ 230249
000230249 005__ 20190509132615.0
000230249 0247_ $$2doi$$a10.5075/epfl-thesis-7879
000230249 02470 $$2urn$$aurn:nbn:ch:bel-epfl-thesis7879-9
000230249 02471 $$2nebis$$a10998354
000230249 037__ $$aTHESIS
000230249 041__ $$aeng
000230249 088__ $$a7879
000230249 245__ $$aRich and Robust Bio-Inspired Locomotion Control for Humanoid Robots
000230249 260__ $$bEPFL$$c2017$$aLausanne
000230249 269__ $$a2017
000230249 300__ $$a293
000230249 336__ $$aTheses
000230249 500__ $$aCo-supervision with: Université catholique de Louvain, Institute of Mechanics, Materials, and Civil Engineering, École doctorale en sciences de l'ingénieur et art de bâtir et urbanisme. UCL, thèse no 612 (2017)
000230249 502__ $$aProf. Kamiar Aminian (président) ; Prof. Auke Ijspeert, Prof. Renaud Ronsse (directeurs) ; Prof. Silvestro Micera, Prof. Paul Fisette, Prof. Hartmut Geyer (rapporteurs)
000230249 520__ $$aBipedal locomotion is a challenging task in the sense that it requires to maintain dynamic balance while steering the gait in potentially complex environments. Yet, humans usually manage to move without any apparent difficulty, even on rough terrains. This requires a complex control scheme which is far from being understood.  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 stimulations (i.e. neural signals), similarly to what is done during human locomotion. Because the human neural circuitry commanding these muscles is not completely known, we make hypotheses about this control scheme to simplify it and progressively refine the corresponding rules.  This thesis thus aims at developing new walking algorithms for humanoid robots in order to obtain fast, human-like and energetically efficient gaits. In particular, gait robustness and richness are two key aspects of this work. In other words, the gaits developed in the thesis can be steered by an external operator, while being resistant to external perturbations. This is mainly tested during blind walking experiments on COMAN, a 95 cm tall humanoid robot. Yet, the proposed controllers can be adapted to other humanoid robots.  In the beginning of this thesis, we adapt and port an existing reflex-based neuromuscular model to the real COMAN platform. When tested in a 2D simulation environment, this model was capable of reproducing stable human-like locomotion. By porting it to real hardware, we show that these neuromuscular controllers are viable solutions to develop new controllers for robotics locomotion.  Starting from this reflex-based model, we progressively iterate and transform the stimulation rules to add new features. In particular, gait modulation is obtained with the inclusion of a central pattern generator (CPG), a neural circuit capable of producing rhythmic patterns of neural activity without receiving rhythmic inputs.  Using this CPG, the 2D walker controllers are incremented to generate 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 steering curvature). In parallel, we also develop a method to automatically learn stimulation networks for a given task and we study how flexible feet affect the gait in terms of robustness and energy efficiency.  In sum, we develop neuromuscular controllers generating human-like gaits with steering capabilities. These controllers recruit three main components: (i) virtual muscles generating torque references at the joint level, (ii) neural signals commanding these muscles with reflexes and CPG signals, and (iii) higher level commands controlling speed and heading.  Interestingly, these developments target humanoid robots locomotion but can also be used to better understand human locomotion. In particular, the recruitment of a CPG during human locomotion is still a matter open to debate. This question can thus benefit from the experiments performed in this thesis.
000230249 6531_ $$aLocomotion Control
000230249 6531_ $$aBiologically-Inspired Robots
000230249 6531_ $$aHumanoid Robots
000230249 6531_ $$aCentral Pattern Generator
000230249 6531_ $$aSensory Feedback
000230249 6531_ $$aGait Modulation
000230249 700__ $$0248140$$g225005$$aVan der Noot, Nicolas Benoît Dominique
000230249 720_2 $$aIjspeert, Auke$$edir.$$g115955$$0241344
000230249 720_2 $$aRonsse, Renaud$$edir.$$g191978$$0(EPFLAUTH)191978
000230249 8564_ $$uhttps://infoscience.epfl.ch/record/230249/files/EPFL_TH7879.pdf$$zn/a$$s27362752$$yn/a
000230249 909C0 $$xU12165$$0252049$$pBIOROB
000230249 909CO $$pthesis$$pthesis-bn2018$$pDOI$$ooai:infoscience.tind.io:230249$$qDOI2$$qGLOBAL_SET$$pSTI
000230249 917Z8 $$x108898
000230249 917Z8 $$x108898
000230249 917Z8 $$x108898
000230249 918__ $$dEDRS$$cIBI-STI$$aSTI
000230249 919__ $$aBIOROB
000230249 920__ $$b2017$$a2017-8-30
000230249 970__ $$a7879/THESES
000230249 973__ $$sPUBLISHED$$aEPFL
000230249 980__ $$aTHESIS