Design of Hand Motion Assist Robot for Rehabilitation Physiotherapy

This paper deals about developing a microcontroller based two-axis robot for human hand physiotherapy for the treatment of paralyzed patients. The interactive two-axis motion robot is designed to fit around patient’s arm and work with the patient to reestablish movements of hand by gently moving it in desired direction. The robot moves the patients hand up and down, left and right using servo motor interfaced with it. The motor is controlled by switching ON/OFF the stator winding. The microcontroller generates the switching pulses of the motor; the angular distance and movements are programmable through keys. The robot actively moves the non-responsive body parts allowing it to be a useful tool in all steps of rehabilitation. Note to Practitioners-Compared with cable-driven humanoid arm, a cable less robot is more accurate because a cable driven robot has some drawbacks due to the mismatch of connections. If the connections are mishandled, there is a chance to occur any severe damages. The main drawback is more expensive, very difficult to maintain and clean. This drawback can be rectified by the proposed method. The main feature of this robot is its mobility function

Improving Dynamics Estimations and Low Level Torque Control Through Inertial Sensing

In 1996, professors J. Edward Colgate and Michael Peshkin invented the cobots as robotic equipment safe enough for interacting with human workers. Twenty years later, collaborative robots are highly demanded in the packaging industry, and have already been massively adopted by companies facing issues for meeting customer demands. Meantime, cobots are still making they way into environments where value-added tasks require more complex interactions between robots and human operators. For other applications like a rescue mission in a disaster scenario, robots have to deal with highly dynamic environments and uneven terrains. All these applications require robust, fine and fast control of the interaction forces, specially in the case of locomotion on uneven terrains in an environment where unexpected events can occur. Such interaction forces can only be modulated through the control of joint internal torques in the case of under-actuated systems which is typically the case of mobile robots. For that purpose, an efficient low level joint torque control is one of the critical requirements, and motivated the research presented here. This thesis addresses a thorough model analysis of a typical low level joint actuation sub-system, powered by a Brushless DC motor and suitable for torque control. It then proposes procedure improvements in the identification of model parameters, particularly challenging in the case of coupled joints, in view of improving their control. Along with these procedures, it proposes novel methods for the calibration of inertial sensors, as well as the use of such sensors in the estimation of joint torques

Control System for 3D Printable Robotic Hand

Humanoid robotics is a growing area of research due to its potential applications in orthosis and prosthesis for human beings. With the currently available technologies, the most advanced robotic hands used in prosthetics or robotics can cost from $11,000 to$90,000, making it inaccessible to the general population of amputees and robotics hobbyists. Most of the features provided by these expensive technologies are superfluous to many users, creating a great gap in cost and services between users and technology. Using the emerging 3D printing technology, my project is to construct a 3D printed robotic hand that can reproduce as many basic functionalities of the advanced expensive hands, while minimizing the cost. The project involves choosing a feasible 3D printed design plan, assembly of the mechanical and electrical components of the robotic hand, the design and implementation of the software interface for intuitive user control of the hand and ease of integrability to existing robotic systems. This new hand will allow mimicking, versatile gripping, human-recognizable gestures, feedback controlled force exertion, and a ROS integrated software interface. This project will further allow students at Union to extend their research in social robotics and human-computer interface by incorporating the inexpensive robotic han

Visual perception system and method for a humanoid robot

A robotic system includes a humanoid robot with robotic joints each moveable using an actuator(s), and a distributed controller for controlling the movement of each of the robotic joints. The controller includes a visual perception module (VPM) for visually identifying and tracking an object in the field of view of the robot under threshold lighting conditions. The VPM includes optical devices for collecting an image of the object, a positional extraction device, and a host machine having an algorithm for processing the image and positional information. The algorithm visually identifies and tracks the object, and automatically adapts an exposure time of the optical devices to prevent feature data loss of the image under the threshold lighting conditions. A method of identifying and tracking the object includes collecting the image, extracting positional information of the object, and automatically adapting the exposure time to thereby prevent feature data loss of the image

Transferring visuomotor learning from simulation to the real world for robotics manipulation tasks

Hand-eye coordination is a requirement for many manipulation tasks including grasping and reaching. However, accurate hand-eye coordination has shown to be especially difficult to achieve in complex robots like the iCub humanoid. In this work, we solve the hand-eye coordination task using a visuomotor deep neural network predictor that estimates the arm's joint configuration given a stereo image pair of the arm and the underlying head configuration. As there are various unavoidable sources of sensing error on the physical robot, we train the predictor on images obtained from simulation. The images from simulation were modified to look realistic using an image-to-image translation approach. In various experiments, we first show that the visuomotor predictor provides accurate joint estimates of the iCub's hand in simulation. We then show that the predictor can be used to obtain the systematic error of the robot's joint measurements on the physical iCub robot. We demonstrate that a calibrator can be designed to automatically compensate this error. Finally, we validate that this enables accurate reaching of objects while circumventing manual fine-calibration of the robot

Design, implementation, and evaluation of a variable stiffness transradial hand prosthesis

We present the design, implementation, and experimental evaluation of a low-cost, customizable, easy-to-use transradial hand prosthesis capable of adapting its compliance. Variable stiffness actuation (VSA) of the prosthesis is based on antagonistically arranged tendons coupled to nonlinear springs driven through a Bowden cable based power transmission. Bowden cable based antagonistic VSA can, not only regulate the stiffness and the position of the prosthetic hand but also enables a light-weight and low-cost design, by the opportunistic placement of motors, batteries, and controllers on any convenient location on the human body, while nonlinear springs are conveniently integrated inside the forearm. The transradial hand prosthesis also features tendon driven underactuated compliant fingers that allow natural adaption of the hand shape to wrap around a wide variety of object geometries, while the modulation of the stiffness of their drive tendons enables the prosthesis to perform various tasks with high dexterity. The compliant fingers of the prosthesis add inherent robustness and flexibility, even under impacts. The control of the variable stiffness transradial hand prosthesis is achieved by an sEMG based natural human-machine interface

Design and development of robust hands for humanoid robots

Design and development of robust hands for humanoid robot