In this paper, we present an electromyography (EMG)-driven assistive hand exoskeleton for spinal-cord-injury (SCI) patients. We developed an active assistive orthosis, called Maestro, which is light, comfortable, compliant, and capable of providing various hand poses. The EMG signals are obtained from a subject's forearm, post-processed, and classified for operating Maestro. The performance of Maestro is evaluated by a standardized hand function test, called the Sollerman hand function test. The experimental results show that Maestro improved the hand function of the SCI patients.

The torque control of finger joints is important for effective hand rehabilitation after neural disorders such as stroke. This paper presents an approach for accurate torque control of finger joints with UT hand exoskeleton. We present (1) how we obtained an accurate kinematics model, (2) how we built the torque actuation model with Bowden cable SEA, and (3) how we controlled the torque of finger joints with the kinematic model and the Bowden cable SEA. We have validated our approach with a testbed finger and results show that the UT hand exoskeleton accurately controls the torque of finger joints.

We present a novel statistical model based control algorithm, called Control in the Reliable Region of a Statistical model (CRROS). First, CRROS builds a statistical model with Gaussian process regression, which provides a prediction function and uncertainty of the prediction. Then, CRROS avoids high-uncertainty regions of the statistical model by regulating the null space of the pseudo inverse solution. The simulation results demonstrate that CRROS drives the states toward high-density and low-noise regions of training data, ensuring high reliability of the model. The experiments with a robotic finger, called Flex-finger, show the potential of CRROS to control robotic systems that are difficult to model, contain constrained inputs, and exhibit heteroscedastic noise output.

This paper presents a systematic method for experimental characterization of Bowden cable friction. A novel tension measurement method using a motion capture system and a spring is introduced. With the tension measurement method, the effects of nine variables on friction are investigated. Experimental results show that i) a combination of 7×19 FEP-coated stainless steel cable and double-sheaths has the highest force transmission efficiency; ii) smaller coefficient of friction material, smaller cable moving speed, shorter cable length, longer clamp distance, stiffer cable/sheath all help to increase the force transmission efficiency; and iii) the static friction pretension ratio only decreases as the coefficient of friction of cable/sheath decreases or as cable/sheath stiffness increases. We have generated guidelines for the Bowden cable performance which may help robotics researchers in choosing materials for the Bowden cables and designing control systems for actuation.

We present a novel statistical model-based control algorithm, called Control in the Reliable Region of a Statistical Model (CRROS). A statistical model is unreliable when its state passes into a region where training data is sparse. CRROS drives the state away from such an unreliable region while pursuing the desired output by taking advantage of the redundancy in the input-output relationships. We validated the performance of CRROS by a simulation with a redundant manipulator and experiments with a robot. In the experiments, a manipulator called the Flex-finger, for which it is challenging to build an analytical model, is controlled to demonstrate the practical effectiveness of the proposed method.

Finger exoskeletons, haptic devices, and augmented reality applications demand an accurate, robust, and fast estimation of finger pose. We present a novel finger pose estimation method using a motion capture system. The method combines system identification and state estimation in a unified framework. The system identification stage investigates the accurate model of a finger, and the state estimation stage tracks the finger pose with the Extended Kalman Filter (EKF) algorithm based on the model obtained in the system identification stage. The algorithm is validated by simulation and experiment. The experimental results show that the method can robustly estimate the finger pose at a high frequency (greater than 1 Khz) in presence of measurement noise, occlusion of markers, and fast movement.

Odometry calibration is a first and essential step to do for a successful navigation because most of control algorithms are based on odomety information. Odometry error can be categorized as systematic and non-systematic error. In this paper, we suggest a novel method to calibrate systematic error using inherent home positioning capability of home cleaning robot. The method is designed for a differential drive type and take advantage of Augmented extended Kalman Fil- ter(AKF) Algorithm to estimates systematic error parameters. Our approach has both characteristics of on-line and off-line. By simulation and experiment, we evaluate the method and the result shows that the proposed method gives odometry error reduction by several times.