Humanoid Motion Description Language

In this paper we propose a description language for specifying motions for humanoid robots and for allowing humanoid robots to acquire motor skills. Locomotion greatly increases our ability to interact with our environments, which in turn increases our mental abilities. This principle also applies to humanoid robots. However, there are great difficulties to specify humanoid motions and to represent motor skills, which in most cases require four-dimensional space representations. We propose a representation framework that includes the following attributes: motion description layers, egocentric reference system, progressive quantized refinement, and automatic constraint satisfaction. We also outline strategies for acquiring new motor skills by learning from trial and error, macro approach, and programming. Then, we outline the development of a new humanoid motion description language called Cybele

Lower body design of the ‘iCub’ a human-baby like crawling robot

The development of robotic cognition and a greater understanding of human cognition form two of the current greatest challenges of science. Within the RobotCub project the goal is the development of an embodied robotic child (iCub) with the physical and ultimately cognitive abilities of a 2frac12 year old human baby. The ultimate goal of this project is to provide the cognition research community with an open human like platform for understanding of cognitive systems through the study of cognitive development. In this paper the design of the mechanisms adopted for lower body and particularly for the leg and the waist are outlined. This is accompanied by discussion on the actuator group realisation in order to meet the torque requirements while achieving the dimensional and weight specifications. Estimated performance measures of the iCub are presented

Humanoid robot walking control on inclined planes

The humanoid bipedal structure is suitable for a assitive robot functioning in the human environment. However, the bipedal walk is a difficult control problem. Walking just on even floor is not satisfactory for the applicability of a humanoid robot. This paper presents a study on bipedal walk on inclined planes. A Zero Moment Point (ZMP) based reference generation technique is employed. The orientation of the feet is adjusted online by a fuzzy logic system to adapt to different walking surface slopes. This system uses a sampling time larger than the one of the joint space position controllers. The average value of the body pitch angle is used as the inputs to the fuzzy logic system. A foot pitch orientation compensator implemented independently for the two feet complements the fuzyy controller. A 12-degrees-of-freedom (DOF) biped robot model is used in the full-dynamics 3-D simulations. Simulations are carried out on even floor and inclined planes with different slopes. The results indicate that the control method presented is successful in enabling the robot to climb slopes of 8.5 degrees (15 percent grade)

Biped robot walking control on inclined planes with fuzzy parameter adaptation

The bipedal structure is suitable for a robot functioning in the human environment, and assuming assistive roles. However, the bipedal walk is a poses a difficult control problem. Walking on even floor is not satisfactory for the applicability of a humanoid robot. This paper presents a study on bipedal walk on inclined planes. A Zero Moment Point (ZMP) based reference generation technique is employed. The orientation of the upper body is adjusted online by a fuzzy logic system to adapt to different walking surface slopes. This system uses a sampling time larger than the one of the joint space position controllers. A newly defined measure of the oscillatory behavior of the body pitch angle and the average value of the pelvis pitch angle are used as inputs to the fuzzy adaptation system. A 12-degrees-of-freedom (DOF) biped robot model is used in the full-dynamics 3-D simulations. Simulations are carried out on even floor and inclined planes with different slopes. The results indicate that the fuzzy adaptation algorithms presented are successful in enabling the robot to climb slopes of 5.6 degrees (10 percent)