HEXOSYS II - Towards realization of light mass robotics for the hand

This research presents a prototype of a direct-driven, optimized and light-mass hand exoskeleton that is designed to fit over the dorsal side of the hand, thus retaining palm free for interaction with real/virtual objects. The link lengths of the proposed Hand EXOskeleton SYStem (HEXOSYS) TT have been selected based on an optimization algorithm. In an attempt to make the design human hand compatible, the actuators of HEXOSYS II have been chosen as a result of series of experiments on human hands of various sizes. The system based on an optimum under-actuated mechanism provides 3 DOF/finger. The resultant motion of the exoskeleton allows the wearer to perform flexion/abduction as well as passive abduction/adduction. Simple and under-actuated mechanisms together with compact mechanical design lead to realize a light mass robotic system. The first prototype of HEXOSYS II has been fabricated. Comprising of four fingers, which are enough to accomplish most of our daily life activities, the system weighs 600 grams. © 2011 IEEE

Modeling and computed torque control of a 6 degree of freedom robotic arm

This paper presents modelling and control design of ED 7220C - a vertical articulated serial arm having 5 revolute joints with 6 Degree Of Freedom. Both the direct and inverse kinematic models have been developed. For analysis of forces and to facilitate the controller design, svstem dvnamics have been formulated. A non-linear control technique, Computed Torque Control (CTC) has been presented. The algorithm, implemented in MATLAB/Simulink, utilizes the derived dynamics as well as linear control techniques. Simulation results dearly demonstrate the efficacy of the presented approach in terms of traiectory tracking Various responses of the arm joints have been recorded to characterize the performance of the control algorithm. The research finds its applications in simulation of advance nonlinear and robust control techniques as well as their implementation on the physical platform. © 2014 IEEE

A multi-robot educational and research framework

Robots have greatly transformed human’s life. Multi-disciplinary research in robotics essentially demands having sophisticated frameworks with diverse range of capabilities ranging from simple tasks like testing of control algorithms to handling complex scenarios like multiple robot coordination. The present research addresses this demand by proposing a reliable, versatile and cheap platform enriched with enormous features. The framework has been conceptualized with three robots having different drive mechanisms, sensing and communication capabilities. The proposed ‘Wanderbot’ family consists of ForkerBot, MasterBot and HexaBot. The ForkerBot is a four-wheeled robot equipped with ultra sonic range finder, wheel encoder, bump sensor, temperature sensor, GSM, GPS and RF communication modules. The robot, having a payload capacity of 8 pounds, supports both Differential and Ackerman drive mechanisms and can be used to validate advanced obstacle avoidance algorithms. The MasterBot is also a wheeled robot with an on-board camera and is skid-steered. The robot finds potential in research on image processing and computer vision and in analysis and validation of algorithms requiring high-level computations like complex path traversal. The third member in the Wander family, HexaBot, is a six-legged robot, which is able to exhibit the movement of tripod gait and can be used for investigating walking and climbing algorithms. The three members of Wander family can communicate with one another, thus making it a good candidate for research on coordinated multi-robots. Additionally, such a prototyped platform with vast attractive features finds potential in an academic and vocational environment

A gripper-like exoskeleton design for robot grasping demonstration

Learning from demonstration (LfD) is a practical method for transferring skill knowledge from a human demonstrator to a robot. Several studies have shown the effectiveness of LfD in robotic grasping tasks to improve the success rate of grasping and to accelerate the development of new robotic grasping tasks. A well-designed demonstration device can effectively represent human grasping motion to transfer grasping skills to robots. In this paper, an improved gripper-like exoskeleton with a data collection system is proposed. First, we present the mechatronic details of the exoskeleton and its motion-tracking system, considering the manipulation flexibility and data acquisition requirements. We then present the capabilities of the device and its data collection system, which collects the position, pose and displacement of the gripper on the exoskeleton. The collected data is further processed by the data acquisition and processing software. Next, we describe the principles of Gaussian mixture model (GMM) and Gaussian mixture regression (GMR) in robot skill learning, which are used to transfer the raw data from demonstrations to robot motions. In the experiment, an optimized trajectory was learned from multiple demonstrations and reproduced on a robot. The results show that the GMR complemented with GMM is able to learn a smooth trajectory from demonstration trajectories with noise

AI and IoT-Enabled smart exoskeleton system for rehabilitation of paralyzed people in connected communities

In recent years, the number of cases of spinal cord injuries, stroke and other nervous impairments have led to an increase in the number of paralyzed patients worldwide. Rehabilitation that can aid and enhance the lives of such patients is the need of the hour. Exoskeletons have been found as one of the popular means of rehabilitation. The existing exoskeletons use techniques that impose limitations on adaptability, instant response and continuous control. Also most of them are expensive, bulky, and requires high level of training. To overcome all the above limitations, this paper introduces an Artificial Intelligence (AI) powered Smart and light weight Exoskeleton System (AI-IoT-SES) which receives data from various sensors, classifies them intelligently and generates the desired commands via Internet of Things (IoT) for rendering rehabilitation and support with the help of caretakers for paralyzed patients in smart and connected communities. In the proposed system, the signals collected from the exoskeleton sensors are processed using AI-assisted navigation module, and helps the caretakers in guiding, communicating and controlling the movements of the exoskeleton integrated to the patients. The navigation module uses AI and IoT enabled Simultaneous Localization and Mapping (SLAM). The casualties of a paralyzed person are reduced by commissioning the IoT platform to exchange data from the intelligent sensors with the remote location of the caretaker to monitor the real time movement and navigation of the exoskeleton. The automated exoskeleton detects and take decisions on navigation thereby improving the life conditions of such patients. The experimental results simulated using MATLAB shows that the proposed system is the ideal method for rendering rehabilitation and support for paralyzed patients in smart communities. © 2013 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramanian” is provided in this record*

Physical human-robot collaboration: Robotic systems, learning methods, collaborative strategies, sensors, and actuators

This article presents a state-of-the-art survey on the robotic systems, sensors, actuators, and collaborative strategies for physical human-robot collaboration (pHRC). This article starts with an overview of some robotic systems with cutting-edge technologies (sensors and actuators) suitable for pHRC operations and the intelligent assist devices employed in pHRC. Sensors being among the essential components to establish communication between a human and a robotic system are surveyed. The sensor supplies the signal needed to drive the robotic actuators. The survey reveals that the design of new generation collaborative robots and other intelligent robotic systems has paved the way for sophisticated learning techniques and control algorithms to be deployed in pHRC. Furthermore, it revealed the relevant components needed to be considered for effective pHRC to be accomplished. Finally, a discussion of the major advances is made, some research directions, and future challenges are presented