scholarly journals Development of a lower extremity wearable exoskeleton with double compact elastic module: preliminary experiments

2017 ◽  
Vol 8 (2) ◽  
pp. 249-258 ◽  
Author(s):  
Yi Long ◽  
Zhi-jiang Du ◽  
Chao-feng Chen ◽  
Wei-dong Wang ◽  
Wei Dong
Keyword(s):  
Lower Extremity ◽  
Human Motion ◽  
Human Robot Interaction ◽  
Experimental Results ◽  
Maximum Torque ◽  
Outer Circle ◽  
Robotic Exoskeleton ◽  
Elastic Module ◽  
Actuation System ◽  
Torsional Spring

Abstract. In this paper, a double compact elastic module is designed and implemented in the lower extremity exoskeleton. The double compact elastic module is composed of two parts, i.e., physical human robot interaction (pHRI) measurement and the elastic actuation system (EAS), which are called proximal elastic module (PEM) and distal elastic module (DEM) respectively. The PEM is used as the pHRI information collection device while the DEM is used as the compliance device. A novel compact parallelogram-like structure based torsional spring is designed and developed. An iterative finite element analysis (FEA) based optimization process was conducted to find the optimal parameters in the search space. In the PEM, the designed torsional spring has an outer circle with a diameter of 60 mm and an inner hole with a diameter of 12 mm, while in the DEM, the torsional spring has the outer circle with a diameter of 80 mm and the inner circle with a diameter of 16 mm. The torsional spring in the PEM has a thickness of 5 mm and a weight of 60 g, while that in the DEM has a thickness of 10 mm and a weight of 80 g. The double compact elastic module prototype is embedded in the mechanical joint directly. Calibration experiments were conducted on those two elastic modules to obtain the linear torque versus angle characteristic. The calibration experimental results show that this torsional spring in the PEM has a stiffness of 60.2 Nm rad−1, which is capable of withstanding a maximum torque of 4 Nm, while that in the DEM has a stiffness of 80.2 Nm rad−1, which is capable of withstanding a maximum torque of 30 Nm. The experimental results and the simulation data show that the maximum resultant errors are 6 % for the PEM and 4 % for the DEM respectively. In this paper, an assumed regression algorithm is used to learn the human motion intent (HMI) based on the pHRI collection. The HMI is defined as the angular position of the human limb joint. A closed-loop position control strategy is utilized to drive the robotic exoskeleton system to follow the human limb's movement. To verify the developed system, experiments are performed on healthy human subjects and experimental results show that this novel robotic exoskeleton can help human users walk, which can be extended and applied in the assistive wearable exoskeletons.

Evaluation of a lower-extremity robotic exoskeleton for people with knee osteoarthritis

2021 ◽  
pp. 1-14 ◽  
Author(s):  
Chris McGibbon ◽  
Andrew Sexton ◽  
Arun Jayaraman ◽  
Susan Deems-Dluhy ◽  
Eric Fabara ◽  
...  
Keyword(s):  
Lower Extremity ◽  
Knee Osteoarthritis ◽  
Robotic Exoskeleton

scholarly journals GazeEMD: Detecting Visual Intention in Gaze-Based Human-Robot Interaction

Robotics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 68
Author(s):  
Lei Shi ◽  
Cosmin Copot ◽  
Steve Vanlanduit
Keyword(s):  
Cognitive Load ◽  
Real World ◽  
Robotic Manipulator ◽  
Similarity Score ◽  
Human Robot Interaction ◽  
Experimental Results ◽  
The Other ◽  
Robot Interaction ◽  
Run Length ◽  
The Real

In gaze-based Human-Robot Interaction (HRI), it is important to determine human visual intention for interacting with robots. One typical HRI interaction scenario is that a human selects an object by gaze and a robotic manipulator will pick up the object. In this work, we propose an approach, GazeEMD, that can be used to detect whether a human is looking at an object for HRI application. We use Earth Mover’s Distance (EMD) to measure the similarity between the hypothetical gazes at objects and the actual gazes. Then, the similarity score is used to determine if the human visual intention is on the object. We compare our approach with a fixation-based method and HitScan with a run length in the scenario of selecting daily objects by gaze. Our experimental results indicate that the GazeEMD approach has higher accuracy and is more robust to noises than the other approaches. Hence, the users can lessen cognitive load by using our approach in the real-world HRI scenario.

scholarly journals A Walking Training System with Customizable Trajectory Designing

2014 ◽  
Vol 5 (1) ◽  
Author(s):  
Shiyang Dong ◽  
Takafumi Matsumaru
Keyword(s):  
Interaction Design ◽  
Detection Method ◽  
Training System ◽  
Human Robot Interaction ◽  
Design Concept ◽  
Experimental Results ◽  
Robot Interaction ◽  
Coordination Training ◽  
Foot Model ◽  
New System

AbstractThis paper shows a novel walking training system for foot-eye coordination. To design customizable trajectories for different users conveniently in walking training, a new system which can track and record the actual walking trajectories by a tutor and can use these trajectories for the walking training by a trainee is developed. We set the four items as its human-robot interaction design concept: feedback, synchronization, ingenuity and adaptability. A foot model is proposed to define the position and direction of a foot. The errors in the detection method used in the system are less than 40 mm in position and 15 deg in direction. On this basis, three parts are structured to achieve the system functions: Trajectory Designer, Trajectory Viewer and Mobile Walking Trainer. According to the experimental results,we have confirmed the systemworks as intended and designed such that the steps recorded in Trajectory Designer could be used successfully as the footmarks projected in Mobile Walking Trainer and foot-eye coordination training would be conducted smoothly.

scholarly journals Human-Following Motion Planning and Control Scheme for Collaborative Robots Based on Human Motion Prediction

2021 ◽  
Author(s):  
Fahad Iqbal Khawaja ◽  
Akira Kanazawa ◽  
Jun Kinugawa ◽  
Kazuhiro Kosuge
Keyword(s):  
Motion Planning ◽  
Human Motion ◽  
Human Robot Interaction ◽  
Collaborative Robots ◽  
Sensing System ◽  
Planning And Control ◽  
Control Scheme ◽  
Active Research ◽  
And Control ◽  
Collaborative Robot

Human-Robot Interaction (HRI) for collaborative robots has become an active research topic recently. Collaborative robots assist the human workers in their tasks and improve their efficiency. But the worker should also feel safe and comfortable while interacting with the robot. In this paper, we propose a human-following motion planning and control scheme for a collaborative robot which supplies the necessary parts and tools to a worker in an assembly process in a factory. In our proposed scheme, a 3-D sensing system is employed to measure the skeletal data of the worker. At each sampling time of the sensing system, an optimal delivery position is estimated using the real-time worker data. At the same time, the future positions of the worker are predicted as probabilistic distributions. A Model Predictive Control (MPC) based trajectory planner is used to calculate a robot trajectory that supplies the required parts and tools to the worker and follows the predicted future positions of the worker. We have installed our proposed scheme in a collaborative robot system with a 2-DOF planar manipulator. Experimental results show that the proposed scheme enables the robot to provide anytime assistance to a worker who is moving around in the workspace while ensuring the safety and comfort of the worker.

scholarly journals Maximum Torque-per-Amp Control for Traction IM Drives: Theory and Experimental Results

2017 ◽  
Vol 53 (1) ◽  
pp. 181-193 ◽  
Author(s):  
Serhiy Bozhko ◽  
Serhii Dymko ◽  
Serhii Kovbasa ◽  
Sergei M. Peresada
Keyword(s):  
Experimental Results ◽  
Maximum Torque

Determination of Variable Stiffness of a Human Elbow for Human-Robot Interaction

2014 ◽  
Author(s):  
Michael Boyarsky ◽  
Megan Heenan ◽  
Scott Beardsley ◽  
Philip Voglewede
Keyword(s):  
Experimental Data ◽  
Disturbance Rejection ◽  
Variable Stiffness ◽  
Important Variable ◽  
Human Motion ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Constant Stiffness ◽  
Stiffness Model ◽  
Motor Model

This paper aims to emulate human motion with a robot for the purpose of improving human-robot interaction (HRI). In order to engineer a robot that demonstrates functionally similar motion to humans, aspects of human motion such as variable stiffness must be captured. This paper successfully determined the variable stiffness humans use in the context of a 1 DOF disturbance rejection task by optimizing a time-varying stiffness parameter to experimental data in the context of a neuro-motor Simulink model. The significant improved agreement between the model and the experimental data in the disturbance rejection task after the addition of variable stiffness demonstrates how important variable stiffness is to creating a model of human motion. To enable a robot to emulate this motion, a predictive stiffness model was developed that attempts to reproduce the stiffness that a human would use in a given situation. The predictive stiffness model successfully decreases the error between the neuro-motor model and the experimental data when compared to the neuro-motor model with a constant stiffness value.

scholarly journals Systematic framework for performance evaluation of exoskeleton actuators

2020 ◽  
Vol 1 ◽  
Author(s):  
Christian Di Natali ◽  
Stefano Toxiri ◽  
Stefanos Ioakeimidis ◽  
Darwin G. Caldwell ◽  
Jesús Ortiz
Keyword(s):  
Evaluation Method ◽  
Inverse Dynamics ◽  
Dynamic Performance ◽  
Human Robot Interaction ◽  
Control Algorithms ◽  
Kinematics And Dynamics ◽  
Actuation System ◽  
And Control ◽  
Systematic Framework ◽  
Actuator Performance

Abstract Wearable devices, such as exoskeletons, are becoming increasingly common and are being used mainly for improving motility and daily life autonomy, rehabilitation purposes, and as industrial aids. There are many variables that must be optimized to create an efficient, smoothly operating device. The selection of a suitable actuator is one of these variables, and the actuators are usually sized after studying the kinematic and dynamic characteristics of the target task, combining information from motion tracking, inverse dynamics, and force plates. While this may be a good method for approximate sizing of actuators, a more detailed approach is necessary to fully understand actuator performance, control algorithms or sensing strategies, and their impact on weight, dynamic performance, energy consumption, complexity, and cost. This work describes a learning-based evaluation method to provide this more detailed analysis of an actuation system for our XoTrunk exoskeleton. The study includes: (a) a real-world experimental setup to gather kinematics and dynamics data; (b) simulation of the actuation system focusing on motor performance and control strategy; (c) experimental validation of the simulation; and (d) testing in real scenarios. This study creates a systematic framework to analyze actuator performance and control algorithms to improve operation in the real scenario by replicating the kinematics and dynamics of the human–robot interaction. Implementation of this approach shows substantial improvement in the task-related performance when applied on a back-support exoskeleton during a walking task.

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