The Rutgers Master II-ND glove is a haptic interface designed for dextrous interactions with virtual environments. The glove provides force feedback up to 16 N each to the thumb, index, middle, and ring fingertips. It uses custom pneumatic actuators arranged in a direct-drive configuration in the palm. Unlike commercial haptic gloves, the direct-drive actuators make unnecessary cables and pulleys, resulting in a more compact and lighter structure. The force-feedback structure also serves as position measuring exoskeleton, by integrating noncontact Hall-effect and infrared sensors. The glove is connected to a haptic-control interface that reads its sensors and servos its actuators. The interface has pneumatic servovalves, signal conditioning electronics, A/D/A boards, power supply and an imbedded Pentium PC. This distributed computing assures much faster control bandwidth than would otherwise be possible. Communication with the host PC is done over an RS232 line. Comparative data with the CyberGrasp commercial haptic glove is presented.

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This paper studies the integration of a human arm with a powered exoskeleton (orthotic device) and its experimental implementation in an elbow joint, naturally controlled by the human. The human-machine interface was set at the neuromuscular level, by using the neuromuscular signal (EMG) as the primary command signal for the exoskeleton system. The EMG signal along with the joint kinematics were fed into a myoprocessor which in turn predicted the muscle moments on the elbow joint. The moment-based control system integrated myoprocessor moment prediction with feedback moments measured at the human arm/exoskeleton and external load/exoskeleton interfaces. The exoskeleton structure under study was a two-link, two-joint mechanism, corresponding to the arm limbs and joints, which was mechanically linked by the human operator. Four indices of performance were used to define the quality of the human/machine integration and to evaluate the operational envelope of the system. Experimental results indicate the feasibility of an EMG-based power exoskeleton system as an integrated human-machine system using high-level neurological signals.

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The legs of animals and walking vehicles are subject to substantial changes in loading in climbing over obstacles. Sense organs (campaniform sensilla) on cockroach legs detect these loads through strains in the exoskeleton. Signals that might be sent by the sense organs during climbing were predicted by applying forces to a finite element model of the leg in directions determined from kinematic studies. The model included an accurate, three-dimensional reconstruction of the leg segment (trochanter) that contains an array of these sensors. Calculated strains generated at different phases of climbing suggest that some groups of sensors in the front leg, that show little activity in walking, are strongly activated in climbing. These groups could provide signals to aid in adapting walking patterns to the changing forces encountered in climbing.

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This correspondence presents a prototype of a powered hand exoskeleton that is designed to fit over the gloved hand of an astronaut and offset the stiffness of the pressurized space suit. This will keep the productive time spent in extravehicular activity from being constrained by hand fatigue. The exoskeleton has a three-finger design, the third and fourth fingers being combined to lighten and simplify the assembly. The motions of the hand are monitored by an array of pressure sensors mounted between theexoskeleton and the hand. Controller commands are determined by a state-of-the-art programmable microcontroller using pressure sensor input. These commands are applied to a PWM driven DC motor array which provides the motive power to move theexoskeleton fingers. The resultant motion of the exoskeleton allows the astronaut to perform both precision grasping tasks with the thumb and forefinger, as well as a power grasp with the entire hand.

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This paper considers adding autonomy to robot hands used in teleoperation systems. Currently, the finger positions of robot hands in teleoperation systems are controlled via a robot master using a Dataglove or exoskeleton. There are several difficulties with this approach: accurate calibration is hard to achieve; robot hands have different capabilities from human hands; and complex force reflection is difficult. In this paper we propose a model of hand teleoperation in which the input device commands the motions of a grasped object rather than the joint displacements of the fingers. To achieve this goal, the hand requires greater autonomy and the capability to perform high-level functions with minimal external input. Therefore, a set of general, primitive manipulation functions that can be performed automatically is defined. These elementary functions control simple rotations and translations of the grasped objects. They are incorporated into a teleoperation system by using a simple input device as a control signal. Preliminary implementations with a Utah/MIT are discussed.

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