Autonomous Bipedal Locomotion with Evolutionary Algorithm

Autonomous Evolutionary Algorithm

Evolutionary algorithms (EA) are randomized heuristic search methods based on the principles of natural evolution (Banzhaf et al., 1998; Goldberg, 1989; Holland, 1975; Bäck, 1996; Koza, 1992). If we know how to describe the problem using the terminology of artificial evolution, the EAs are quite easy to apply. Actually, the search for solution(s) is transformed into a search for the best EA set...

Bipedal Locomotion Control with Rhythmic Neural Circuits

This paper present a biologically inspired rhythmic neural circuitries that enable robustness bipedal locomotion control, introduction of phase resetting yield to perturbations; and the introduction of a patterns switching mechanism allows behavior adaptions that enable the handling of greater perturbational forces.

Towards Muscle Driven Bipedal Locomotion

Multi-contact bipedal robotic locomotion

This paper presents a formal framework for achieving multi-contact bipedal robotic walking, and realizes this methodology experimentally on two robotic platforms: AMBER2 and ATRIAS. Inspired by the key feature encoded in human walking— multi-contact behavior—this approach begins with the analysis of human locomotion and uses it to motivate the construction of a hybrid system model representing ...

Bipedal locomotion in crawling cells.

Many complex cellular processes from mitosis to cell motility depend on the ability of the cytoskeleton to generate force. Force-generating systems that act on elastic cytoskeletal elements are prone to oscillating instabilities. In this work, we have measured spontaneous shape and movement oscillations in motile fish epithelial keratocytes. In persistently polarized, fan-shaped cells, retracti...