Human biped locomotion is an ultimate style of biological movement that is a highly evolved function. Biped locomotion by robots is a dream to be attained by the most highly evolved or integrated technology, and research on this has a history of over 30 years. Many methods of generating gaits have been proposed. There has been a tendency to reduce the complicated dynamics of a walking robot to ...

Humanoid robots have been extensively studied in the last few years. The motivation for this study is that bipedal locomotion is superior to wheeled approaches on real terrain and situations where robots accompany or replace humans. Some examples are, on the development of human assisting device, such as prosthetics, orthotics, and devices for rehabilitation, rescue of wounded troops, maidens, ...

This paper deals with dynamic optimization of biped locomotion. The main focus of this research is motion optimization of double support phase. The optimization problem is dealt by using Pontryagins Maximum Principal. For motion optimization of double support phase, the closed kinematic chain has been considered to be opened at appropriate joint and the components of ground reaction forces has ...

This paper deals with the generation of dynamically balanced gaits of a ditch-crossing biped robot having seven degrees of freedom (DOFs). Three different approaches, namely analytical, neural network (NN)based and fuzzy logic (FL)-based, have been developed to solve the said problem. The former deals with the analytical modeling of the ditch-crossing gait of a biped robot, whereas the latter t...

This paper proposes a central pattern generators based control architecture using a frequency adaptive oscillator for learning to locomotion of humanoid robot. Central pattern generators are biological neural networks that can produce coordinated multidimensional rhythmic signals, under the control of simple input signals. They are found both in vertebrate and invertebrate animals for the contr...

Controlling a biped robot with a high degree of freedom to achieve stable and straight movement patterns is a complex problem. With growing computational power of computer hardware, high resolution real time simulation of such robot models has become more and more applicable. This paper presents a novel approach to generate bipedal gait for humanoid locomotion. This approach is based on modifie...

A real-time joint trajectory generation strategy for the dynamic walking biped ”Lucy” [1, 2] is proposed. This trajectory planner generates dynamically stable motion patterns by using a set of objective locomotion parameters as its input, and by tuning and exploiting the natural upper body dynamics. The latter can be determined and manipulated by using the angular momentum equation. Basically, ...

Next generation robots will be required to operate alongside people in complex environments that are primarily designed for humans. Humanoid robots present themselves as the logical choice for such tasks. In order to successfully navigate within human environments, humanoid robots require complex dynamic movements with robust balance maintenance on uneven terrain and during unexpected balance d...

In this paper, a bilaterally decoupled neural controller was used to achieve stable locomotion in a 6 DOF biped robot with 5 links, implemented in a virtual physics-based simulation environment. The neural controller consists of an independent neural rhythm generator for each leg. The rhythm generator is a recurrent neural network, whose weights are optimized by a genetic algorithm and are iden...

Traditional methods for robotic biped locomotion employing stiff actuation display low energy efficiency and high sensitivity to disturbances. Legged locomotion can be modelled as an hybrid system, where continuous dynamic flows, such as the single or double support stages, are interrupted by discrete jumps, such as heelstrike or lift-off. Traditional control systems are not suited to deal with...