HERO Glove

Non-repetitive manipulation tasks that are easy for humans to perform are difficult for autonomous robots to execute. The Haptic Exoskeletal Robot Operator (HERO) Glove is a system designed for users to remotely control robot manipulators whilst providing sensory feedback to the user. This realistic haptic feedback is achieved through the use of toroidal air-filled actuators that stiffen up around the user’s fingers. Tactile sensor data is sent from the robot to the HERO Glove, where it is used to vary the pressure in the toroidal actuators to simulate the sense of touch. Curvature sensors and inertial measurement units are used to capture the glove’s pose to control the robot

3D printed pneumatic soft actuators and sensors: their modeling, performance quantification, control and applications in soft robotic systems

Continued technological progress in robotic systems has led to more applications where robots and humans operate in close proximity and even physical contact in some cases. Soft robots, which are primarily made of highly compliant and deformable materials, provide inherently safe features, unlike conventional robots that are made of stiff and rigid components. These robots are ideal for interacting safely with humans and operating in highly dynamic environments. Soft robotics is a rapidly developing field exploiting biomimetic design principles, novel sensor and actuation concepts, and advanced manufacturing techniques. This work presents novel soft pneumatic actuators and sensors that are directly 3D printed in one manufacturing step without requiring postprocessing and support materials using low-cost and open-source fused deposition modeling (FDM) 3D printers that employ an off-the-shelf commercially available soft thermoplastic poly(urethane) (TPU). The performance of the soft actuators and sensors developed is optimized and predicted using finite element modeling (FEM) analytical models in some cases. A hyperelastic material model is developed for the TPU based on its experimental stress-strain data for use in FEM analysis. The novel soft vacuum bending (SOVA) and linear (LSOVA) actuators reported can be used in diverse robotic applications including locomotion robots, adaptive grippers, parallel manipulators, artificial muscles, modular robots, prosthetic hands, and prosthetic fingers. Also, the novel soft pneumatic sensing chambers (SPSC) developed can be used in diverse interactive human-machine interfaces including wearable gloves for virtual reality applications and controllers for soft adaptive grippers, soft push buttons for science, technology, engineering, and mathematics (STEM) education platforms, haptic feedback devices for rehabilitation, game controllers and throttle controllers for gaming and bending sensors for soft prosthetic hands. These SPSCs are directly 3D printed and embedded in a monolithic soft robotic finger as position and touch sensors for real-time position and force control. One of the aims of soft robotics is to design and fabricate robotic systems with a monolithic topology embedded with its actuators and sensors such that they can safely interact with their immediate physical environment. The results and conclusions of this thesis have significantly contributed to the realization of this aim

RoboGlove-A Grasp Assist Device for Earth and Space

The RoboGlove is an assistive device that can augment human strength, endurance or provide directed motion for use in rehabilitation. RoboGlove is a spinoff of the highly successful Robonaut 2 (R2) system developed as part of a partnership between General Motors and NASA. This extremely lightweight device employs an actuator system based on the R2 finger drive system to transfer part or the entire grasp load from human tendons to artificial ones contained in the glove. Steady state loads ranging from 15 to 20 lbs. and peaks approaching 50 lbs. are achievable. Work is underway to integrate the RoboGlove system with a space suit glove to add strength or reduce fatigue during spacewalks. Tactile sensing, miniaturized electronics, and on-board processing provide sufficient flexibility for applications in many industries. The following describes the design, mechanical/electrical integration, and control features of the glove in an assembly-line configuration and discusses work toward the space suit application

Soft Gloves: A Review on Recent Developments in Actuation, Sensing, Control and Applications

Interest in soft gloves, both robotic and haptic, has enormously grown over the past decade, due to their inherent compliance, which makes them particularly suitable for direct interaction with the human hand. Robotic soft gloves have been developed for hand rehabilitation, for ADLs assistance, or sometimes for both. Haptic soft gloves may be applied in virtual reality (VR) applications or to give sensory feedback in combination with prostheses or to control robots. This paper presents an updated review of the state of the art of soft gloves, with a particular focus on actuation, sensing, and control, combined with a detailed analysis of the devices according to their application field. The review is organized on two levels: a prospective review allows the highlighting of the main trends in soft gloves development and applications, and an analytical review performs an in-depth analysis of the technical solutions developed and implemented in the revised scientific research. Additional minor evaluations integrate the analysis, such as a synthetic investigation of the main results in the clinical studies and trials referred in literature which involve soft gloves

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

Piezoresistive 3D graphene-PDMS spongy pressure sensors for IoT enabled wearables and smart products

Recently, 3D porous graphene-polymer composite-based piezoresistive sensors have drawn great interest of researchers in the field of flexible electronics owing to their ultralightweight nature, compressability, robustness, and excellent electromechanical properties. In this work, we present a facile recipe for developing repeatable, reliable, and linear 3D graphene-PDMS spongy sensors for internet-of-things (IoT)-enabled wearable systems and smart consumer products. Fundamental morphological characterization and sensing performance assessment of the piezoresistive 3D graphene-polymer sensors were conducted to establish its suitability for the development of squeezable, flexible, and skin-mountable human motion sensors. The density and porosity of the sponges were determined to be 250 mgcm-3 and 74% respectively. Mechanical compressive loading tests conducted on the sensors showed an average elastic modulus as low as ~56.7 kPa. Dynamic compressive force-resistance change response tests conducted on four identical sensors revealed a linear piezoresistive response (in the compressive load range 0.42–3.90 N) with an average force sensitivity of 0.209±0.027 N-1. In addition, an accelerated lifetime test comprising 1500 compressive loading cycles (at 3.90 N uniaxial compressive loading) was conducted to demonstrate the long-term reliability of the sensor. To test the applicability of the sensors in smart wearables, four identical graphene-PDMS sponges were configured on the fingertip regions of a soft nitrile glove to develop a pressure sensing smart glove for real-time haptic pressure monitoring. The sensors were also integrated into Philips electronic shaver to realize smart shaving applications with the ability to monitor shaving motions. Furthermore, the readiness of our system for next-generation IoT-enabled applications was demonstrated by integrating the smart glove with an embedded system software utilizing the Arduino-Uno platform. The system was capable of identifying real-time qualitative pressure distribution across the fingertips while grasping daily life objects, thus establishing the suitability of such sensors for next-generation wearables for prosthetics, consumer devices, and personalized healthcare monitoring devices

Evaluation of Manually Completed Manufacturing Assembly Processes Through a Wearable Force and Motion Sensing System Integrated Into a Glove

The objective of this research is to model the relationship between force, sound, and motion signals in manual assembly environments through a wearable sensor glove and the resultant quality of vehicle connections made on the assembly line. Many tasks in production assembly are still completed manually due to the intuition needed by the associate, complex automation steps, or time constraints. This is largely observed in automotive assembly environments. With the amount of variability in manually completed processes, the possibility for error increases. These processes include hose and electrical connections which can loosen over time after passing initial quality testing, resulting in costly, time-consuming rework and a diminished brand image. It is the intent of this work to utilize multidimensional operator force signatures and movements exhibited to understand the primary forces acting in the direction of the connector locking and additional measured forces acting in other directions. The sensor signals feed into the classification algorithm for rapid postprocessing to enable real-time feedback indicating a completed connection or a connection that needs further investigation. These classifications can later act as a steppingstone for automating manually completed manufacturing processes by implementing the findings into autonomous systems to yield an automatic verification of the process. This research captured data physically exerted by the operator as a means of accountable process quality evaluation where there are limited marketable products and research. The work also introduced a sensor glove system capable of capturing operator applied shear force in a robust and durable way fit for a manufacturing environment. Marketed products and research shear force sensing are extremely limited in breadth, and force sensing gloves are unsuitable for an assembly environment due to cost, measurement capabilities, durability, and/or operator encroachment. The sensing system developed in this research is coupled with a classification algorithm capable of discerning incomplete or rework connections from successful ones demonstrated on an OEM assembly line. The developed sensor glove capable of capturing shear and normal force, acceleration, and gyroscopic information was successfully tested on an OEM assembly line for 250+ vehicles of work. This includes the completion of hard plastic connections, tool usage, and tasks completed outside of the takt. Five classification models using the gathered data yielded accuracies of 91% or above using a 60/40 train/test split. The best performing model, Na¨ıve Bayes, achieved a balanced accuracy of 97.6%

Stretchable capacitive tactile skin on humanoid robot fingers - first experiments and results

A stretchable tactile sensor skin has been demonstrated on the dorsal side of a robotic hand for the first time. The sensors can detect normal pressures on the same scale as human skin but also in excess of 250 kPa and withstand strains in excess of 15%. Using tactile information from the sensors mounted on a glove worn by a humanoid robot's hand, obstacle detection and surface reconstruction tasks were successfully completed in order to demonstrate the performance of the sensors under applied strains and pressure

A Weft Knit Data Glove

Rehabilitation of stoke survivors can be expedited by employing an exoskeleton. The exercises are designed such that both hands move in synergy. In this regard often motion capture data from the healthy hand is used to derive control behaviour for the exoskeleton. Therefore, data gloves can provide a low-cost solution for the motion capture of the joints in the hand. However, current data gloves are bulky, inaccurate or inconsistent. These disadvantages are inherited because the conventional design of a glove involves an external attachment that degrades overtime and causes inaccuracies. This paper presents a weft knit data glove whose sensors and support structure are manufactured in the same fabrication process thus removing the need for an external attachment. The glove is made by knitting multifilament conductive yarn and an elastomeric yarn using WholeGarment technology. Furthermore, we present a detailed electromechanical model of the sensors alongside its experimental validation. Additionally, the reliability of the glove is verified experimentally. Lastly, machine learning algorithms are implemented for classifying the posture of hand on the basis of sensor data histograms