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In this paper, we present a novel use of haptic information in extended range telepresence, the wide-area haptic guidance. It consists of force and position signals applied to the user's hand in order to improve safety, accuracy, and speed in some telepresent tasks. Wide-area haptic guidance assists the user in reaching a desired position in a remote environment of arbitrary size without degrading...
In this paper, Dual Differential Rheological Actuator (DDRA) is discussed. DDRA is an actuator with high performance torque and position control characteristics, in addition to versatile, safe, robust and easily controllable robotic interactions. DDRA's design is based on the use of two magnetorheological (MR) brakes coupled to an EM motor and to the system's output through a dual differential mechanism...
This article presents the experimental verification of kinematic and dynamic models of the PHANToM Premium 1.0 haptic device. Dynamic properties are evaluated, in order to apply nonlinear control techniques and evaluation of manipulability and energy in real-time. Position kinematics and differential kinematics are validated for the trajectory planning by using joint control techniques for local haptic...
Human-computer/machine interface research benefits from accurate human arm models for stability analysis, control, and system design. The current study developed models for human arm dynamics and variability specific to stylus-based kinesthetic haptic interfaces. Data from nine human subjects (5 male, 4 female, ages 20-30) were collected using a three degree-of-freedom haptic device in the X, Y, and...
This paper presents the design and characteristics of a five-fingered haptic interface robot: HIRO III. The aim of the paper is to provide a high-precision force representation at the five human fingertips. HIRO III consists of a 15-degree-of-freedom (DOF) haptic hand, a 6 DOF interface arm and a control system. The haptic interface, which consists of a robot arm and hand, can be used in a large workspace...
This paper presents a method by which a robot can learn through observation to perform a collaborative manipulation task, namely lifting an object. The task is first demonstrated by a user controlling the robot's hand via a haptic interface. Learning extracts statistical redundancies in the examples provided during training by using Gaussian Mixture Regression and Hidden Markov Model. Haptic communication...
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