Need to develop human body's posture supervised robots, gave the push to researchers to think over dexterous design of exoskeleton robots. It requires to develop quantitative techniques to assess motor function and generate the command for the robots to act accordingly with complex human structure. In this paper, we present a new technique for the upper limb power exoskeleton robot in which load is gripped by the human subject and not by the robot while the robot assists. Main challenge is to find non-biological signal based human desired motion intention to assist as needed. For this purpose, we used newly developed Muscle Circumference Sensor (MCS) instead of electromyogram (EMG) sensors. MCS together with the force sensors is used to estimate the human interactive force from which desired human motion is extracted using adaptive Radial Basis Function Neural Network (RBFNN). Developed Upper limb power exoskeleton has seven degrees of freedom (DOF) in which five DOF are passive while two are active. Active joints include shoulder and elbow in Sagittal plane while abduction and adduction motion in shoulder joint is provided by the passive joints. To ensure high quality performance model reference based adaptive impedance controller is employed. Exoskeleton performance is evaluated experimentally by a neurologically intact subject which validates the effectiveness.

This study developed the Hanyang Exoskeleton Assistive Robot (HEXAR)-CR50 aimed at improving muscle strength of the wearer while transporting a load. The developed exoskeleton robot HEXAR-CR50 has 7 degrees of freedom (DOF) for one foot, 3-DOF for the hip joints, 1-DOF for the knee joints, and 3-DOF for the ankle joints. Through functional analysis of each human joint, two DOFs were composed of active joints using an electric motor developed in an under-actuated form with heightened efficiency. The rest of the DOFs were composed of passive or quasi-passive joints to imitate human joints. The control of the exoskeleton robot was based on the physical human-robot interaction. In order to verify the performance of the developed HEXAR-CR50, muscle activity was measured using electromyography, vGRF was measured using F-Scan sensor. The experimental results showed that %MVIC was reduced against the external load applied, while GRF had a decrement rate, compared with the external load when the exoskeleton was worn, which verified the performance in accordance with the development objective of load carrying. A muscle strength augment effect from the developed wearable robot was verified.

Need to develop human body's posture supervised robots, gave the push to researchers to think over dexterous design of exoskeleton robots. It requires to develop quantitative techniques to assess motor function and generate the command for the robots to act accordingly with complex human structure. In this paper, we focus on developing new technique for the upper limb power exoskeleton in which load is handled by the human subject and not by the robot. Main challenge along with the design complexity is to find the desired human motion intention and to develop an algorithm to assist as needed accordingly. For this purpose, we used newly developed Muscle Circumference Sensor (MCS) instead of electromyogram (EMG) sensors. MCS together with the load cells is used to estimate the desired human intention by which desired trajectory is generated. The desired trajectory is then tracked by passivity based adaptive control technique. Developed Upper limb power exoskeleton has seven degrees of freedom (DOF) in which five are passive and two are active. Active joints include shoulder and elbow, powered by electric motors and move in Sagittal plane while abduction and adduction motion in shoulder joint is provided by the passive joint. Performance of the exoskeleton is evaluated experimentally by a neurologically intact subject. The results show that after adjusting the motion intention recognition algorithm for the subject, the robot assisted effectively and the subject only felt nominal load regardless of the weight in hand.

In this paper, we propose and experimentally test a dynamic model-based force controller for the motion of upper limb exoskeleton robot. The system is composed of 3 degrees of freedom using an electrical actuator. This system is mainly controlled by the multi-axis force sensor signals. These are used to generate desired torques for driving the robot system. However, singularities exist when force signals in the Cartesian coordinate system are transformed to torques in the joint coordinate system. So we applied the damped least squares method. In handling loads, torque compensation regarding the weight of the object is required. Therefore, we installed the multi-axis force sensor at the robot end effector. It measures the interaction forces between the exoskeleton and the load. To compensate for the handling object, we used the static model. We performed control stability and load handling experiments to verify the effectiveness of the controller. With these experiments, we confirmed the effectiveness of the proposed controller.

This paper describes the compact design of a torque sensor using optical technique and its fabrication for wearable and quadruped robots. The torque generated can only be measured by placing torque sensor on each joint, which makes the joint bulky and heavy, adding extra load to be carried by robot. In this paper we proposed a compact and light weight design for the torque sensor. This sensor can measure torque of robots links by detecting torsion. The optical technique is used to detect angular displacement in the joints. The torque can be calculated by using torque and twist-angle relation. The proposed design is simulated by FEM software (ANSYS) and it shows successful measurements of the torque with a load capacity of 50 Nm, which is sufficient for the torque generated in wearable and quadruped robots joints. The designed optical torque sensor is manufactured by EDM (Electrical Discharge Machining). The optical torque sensor was calibrated and several experiments were conducted to ratify its feasibility with the wearable and quadruped robots.

This paper presents the procedure and the results of the multi-objective optimization of the design of a seven degrees of freedom (7DOF) robot manipulator for better global performance, which pertains to the global conditioning, global manipulability, and structural length indices. The Taguchi method was used to reduce the time needed to analyze the global performance of the robot manipulator. Grey relational analysis was performed to deal with the multiple performances of the manipulator by converting multiple responses into a single response with a Grey relational grade. Analysis of variance (ANOVA) was done to analyze the effect of the link parameters on the performance of the robot manipulator, and to determine which link parameter affects the performance of the robot manipulator. The results of the optimization of the prototype robot manipulator and the comparison of the optimization results with those of an earlier study are presented herein.