1,720,979 research outputs found
A highly stretchable artificial sensitive skin using EIT
No full textCurrent sensing technologies are very challenging to implement over 3D sur- faces and present wires within the active sensing area, limiting their overall deformation and creat- ing fragility. These critical limitations hinder their integration for arti cial skin purposes, while a soft and low-cost solution is needed, especially on high-deformable areas
A Quantitative Evaluation of Drive Patterns in Electrical Impedance Tomography
No full textAbstract. Electrical Impedance Tomography (EIT) is a method used to display, through an image, the conductivity distribution inside a domain by using measurements taken from electrodes placed at its periphery. This paper presents our prototype of a stretchable touch sensor, which is based on the EIT method. We then test its performance by comparing voltage data acquired from testing with two different materials, using the performance parameters Signal- to-Noise Ratio (SNR), Boundary Voltage Changes (BVC) and Singular Value Decomposition (SVD). The paper contributes to the literature by demonstrating that, depending on the present stimuli position over the conductive domain, the selection of electrodes on which current injection and voltage reading are performed, can be chosen dynamically resulting in an improved quality of the reconstructed image and system performance
A Quantitative Evaluation of Drive Pattern Selection for Optimizing EIT-Based Stretchable Sensors
Full text linkElectrical Impedance Tomography (EIT) is a medical imaging technique that has been recently used to realize stretchable pressure sensors. In this method, voltage measurements are taken at electrodes placed at the boundary of the sensor and are used to reconstruct an image of the applied touch pressure points. The drawback with EIT-based sensors, however, is their low spatial resolution due to the ill-posed nature of the EIT reconstruction. In this paper, we show our performance evaluation of different EIT drive patterns, specifically strategies for electrode selection when performing current injection and voltage measurements. We compare voltage data with Signal-to-Noise Ratio (SNR) and Boundary Voltage Changes (BVC), and study image quality with Size Error (SE), Position Error (PE) and Ringing (RNG) parameters, in the case of one-point and two-point simultaneous contact locations. The study shows that, in order to improve the performance of EIT based sensors, the electrode selection strategies should dynamically change correspondingly to the location of the input stimuli. In fact, the selection of one drive pattern over another can improve the target size detection and position accuracy up to 4.7% and 18%, respectively
Development of a High-Speed Current Injection and Voltage Measurement System for Electrical Impedance Tomography-Based Stretchable Sensors
Full text linkElectrical impedance tomography (EIT) is an imaging method that can be applied over stretchable conductive-fabric materials to realize soft and wearable pressure sensors through current injections and voltage measurements at electrodes placed at the boundary of a conductive medium. In common EIT systems, the voltage data are serially measured by means of multiplexers, and are hence collected at slightly different times, which affects the real-time performance of the system. They also tend to have complicated hardware, which increases power consumption. In this paper, we present our design of a 16-electrode high-speed EIT system that simultaneously implements constant current injection and differential potential measurements. This leads to a faster, simpler-to-implement and less-noisy technique, when compared with traditional EIT approaches. Our system consists of a Howland current pump with two multiplexers for a constant DC current supply, and a data acquisition card. It guarantees a data collection rate of 78 frames/s. The results from our conductive stretchable fabric sensor show that the system successfully performs voltage data collection with a mean signal-to-noise ratio (SNR) of 55 dB, and a mean absolute deviation (MAD) of 0.5 mV. The power consumption can be brought down to 3 mW; therefore, it is suitable for battery-powered applications. Finally, pressure contacts over the sensor are properly reconstructed, thereby validating the efficiency of our EIT system for soft and stretchable sensor applications
Planetary robotic vision processing for terrain assessment.
Full text linkVision based object detection is a key feature within planetary rover navigation which facilitates several functions such as hazard avoidance, localization and path planning. Most of the current research is based on stereoscopic vision or multiple cameras strategically placed along the rover chassis that perform one specific function. This works for large rovers with sufficient processing power, however such resources would not be very practical for small or micro-rovers. This thesis aims to extract terrain surface information from a single camera mounted on a micro-rover such as the Surrey Mobile Autonomy and Robotics Testbed (SMART) based on minimal computational resources. The terrain surface information can provide feature inputs to other on-board navigation functions such as the Planetary Monocular Simultaneous Localisation and Mapping (PM-SLAM) and constellation matching. The detected terrain surface can also be of scientific interest due of the geometrical characteristics produced from this research. This research aims to improve the processing speed of the Guidance Navigation and Control (GNC) system using low level 2D image processing techniques. The methods employed result in a faster "perception stage" of the GNC with lower processing power requirements, creating structural information, shape descriptors and cognitive segmentation/classification of the rover’s surrounding environment. Although the initial application of this research is for planetary rovers, the research outcome is envisaged to be relevant, and hence transferable, to other vehicle navigation problems used on land, air or under water
A Bioinspired Underactuated Dual Tendon-Based Adaptive Gripper for Space Applications
No full textHands are one of the most intricate elements of a humanoid due to their role as end-effectors interacting with their non-linear surrounding environment. This paper aims to present the design of a bioinspired underactuated robotic hand with an improved dexterity that is capable of adaptive grasping and manipulation of a wide-range of objects using a dual-tendon mechanism. The proposed design is focused on the key elements of scalability, modularity, ease of fabrication and cost efficiency to meet several imperative constraints of space applications. These features are achieved by introducing a novel actuation mechanism, manufacturing methods, and component design. In particular, monolithic finger modules are fabricated by fusing and integrating both hard and soft materials analogous to bones wrapped in muscles using economical and readily-available materials and machines (intermediate 3D printer). Weight-to-power ratio, actuation optimisation, design trade-offs, and various potential applications of the proposed adaptive hand is discussed in this paper. Furthermore, the prototype is subjected to evaluation of its performance in different scenarios that ultimately confirms its improved dexterity and gripping power compared to the literature
Controlling of Pneumatic Muscle Actuator Systems by Parallel Structure of Neural Network and Proportional Controllers (PNNP)
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