SUBAR (Sogang University's Biomedical Assistive Robot), an advanced version of the EXPOS [1-2], is a wearable robot developed for assisting physically impaired people. It provides a human with assistive forces controlled by the human intention. Since it applies geared DC motors, however, the control efforts are used mainly to overcome the resistive forces caused by the friction, the damping and the inertia in the actuator. In this paper, such undesired properties are rejected by applying a flexible transmission. With the proposed method, it is intended that an actuator exhibits zero impedance without friction and generates the desired torques precisely. Since the actuation system of SUBAR has a large model variation due to human-robot interaction, a control algorithm for the flexible transmission is designed based on robust control theory. In this paper, the mechanical design of SUBAR including the flexible transmission and its associated control algorithm are discussed. They are also verified by experiments.

In this paper we present a novel method of designing multi-fingered robotic hands using tasks composed of both finite and infinitesimal motion. The method is based on representing the robotic hands as a kinematic chain with a tree topology. We represent finite motion using Clifford algebra and infinitesimal motion using Lie algebra to perform finite dimensional kinematic synthesis of the multifingered mechanism. This allows tasks to be defined not only by displacements, but also by the velocity and acceleration at different positions for the design of robotic hands. The additional information enables an increased local approximation of the task at critical positions, as well as contact and curvature specifications. An example task is provided using an experimental motion capture system and we present the design of a robotic hand for the task using a hybrid Genetic Algorithm/LevenbergMarquadt solver.

Abstract²This paper seeks to define the mechanisms by which the human motor system finds optimal reaching solutions, when one of the arm joints is locked in place. Specifically, the paper studies how people solve the problem of motion planning when they lose the ability to move their elbow joint. Our hypothesis is based on the idea that the governing rules of motion planning will be consistent even under the given joint constraint, i.e. the hand will follow the shortest path with a bell±shaped velocity profile, while reaching from a start to an end position. We present an experimental protocol with human subjects to compare their hand paths with the geodesic curve in Euclidean space. The speed profiles of these trajectories are also compared to the modified output of the so±called minimum jerk model of Flash and Hogan.Our results indicate that arm reaching paths with an elbow joint constraint at a certain angle closely follow the geodesic and has a bell shaped speed profile. The future work involves extending this research to the shoulder and wrist joints.

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