NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

Electronic systems for the restoration of the sense of touch in upper limb prosthetics

In the last few years, research on active prosthetics for upper limbs focused on improving the human functionalities and the control. New methods have been proposed for measuring the user muscle activity and translating it into the prosthesis control commands. Developing the feed-forward interface so that the prosthesis better follows the intention of the user is an important step towards improving the quality of life of people with limb amputation. However, prosthesis users can neither feel if something or someone is touching them over the prosthesis and nor perceive the temperature or roughness of objects. Prosthesis users are helped by looking at an object, but they cannot detect anything otherwise. Their sight gives them most information. Therefore, to foster the prosthesis embodiment and utility, it is necessary to have a prosthetic system that not only responds to the control signals provided by the user, but also transmits back to the user the information about the current state of the prosthesis. This thesis presents an electronic skin system to close the loop in prostheses towards the restoration of the sense of touch in prosthesis users. The proposed electronic skin system inlcudes an advanced distributed sensing (electronic skin), a system for (i) signal conditioning, (ii) data acquisition, and (iii) data processing, and a stimulation system. The idea is to integrate all these components into a myoelectric prosthesis. Embedding the electronic system and the sensing materials is a critical issue on the way of development of new prostheses. In particular, processing the data, originated from the electronic skin, into low- or high-level information is the key issue to be addressed by the embedded electronic system. Recently, it has been proved that the Machine Learning is a promising approach in processing tactile sensors information. Many studies have been shown the Machine Learning eectiveness in the classication of input touch modalities.More specically, this thesis is focused on the stimulation system, allowing the communication of a mechanical interaction from the electronic skin to prosthesis users, and the dedicated implementation of algorithms for processing tactile data originating from the electronic skin. On system level, the thesis provides design of the experimental setup, experimental protocol, and of algorithms to process tactile data. On architectural level, the thesis proposes a design ow for the implementation of digital circuits for both FPGA and integrated circuits, and techniques for the power management of embedded systems for Machine Learning algorithms

A novel elastic sensor sheet for pressure injury monitoring: design, integration, and performance analysis

This study presents the SENSOMATT sensor sheet, a novel, non-invasive pressure monitoring technology intended for placement beneath a mattress. The development and design process of the sheet, which includes a novel sensor arrangement, material selection, and incorporation of an elastic rubber sheet, is investigated in depth. Highlighted features include the ability to adjust to varied mattress sizes and the incorporation of AI technology for pressure mapping. A comparison with conventional piezoelectric contact sensor sheets demonstrates the better accuracy of the SENSOMATT sensor for monitoring pressures beneath a mattress. The report highlights the sensor network’s cost-effectiveness, durability, and enhanced data measurement, alongside the problems experienced in its design. Evaluations of performance under diverse settings contribute to a full understanding of its potential pressure injury prediction and patient care applications. Proposed future paths for the SENSOMATT sensor sheet include clinical validation, more cost and performance improvement, wireless connection possibilities, and improved long-term monitoring data analysis. The study concludes that the SENSOMATT sensor sheet has the potential to transform pressure injury prevention techniques in healthcare.This work was carried out under the SensoMatt project, grant agreement no. CENTRO-01-0247-FEDER-070107, co-financed by European Funds (FEDER) by CENTRO2020.info:eu-repo/semantics/publishedVersio

Haptics in Robot-Assisted Surgery: Challenges and Benefits

Robotic surgery is transforming the current surgical practice, not only by improving the conventional surgical methods but also by introducing innovative robot-enhanced approaches that broaden the capabilities of clinicians. Being mainly of man-machine collaborative type, surgical robots are seen as media that transfer pre- and intra-operative information to the operator and reproduce his/her motion, with appropriate filtering, scaling, or limitation, to physically interact with the patient. The field, however, is far from maturity and, more critically, is still a subject of controversy in medical communities. Limited or absent haptic feedback is reputed to be among reasons that impede further spread of surgical robots. In this paper objectives and challenges of deploying haptic technologies in surgical robotics is discussed and a systematic review is performed on works that have studied the effects of providing haptic information to the users in major branches of robotic surgery. It has been tried to encompass both classical works and the state of the art approaches, aiming at delivering a comprehensive and balanced survey both for researchers starting their work in this field and for the experts

Ultrasound Orientation Sensor

Ultrasound (US) is a painless method of gaining a visual representation of the internal structures of a human body. It is used to look for diseases and other abnormalities. In effort to minimize and eliminate the amount of error generated by the operation of an US machine, a team of WPI students conducted research into the causes and reasons as to why these problems are not resolved. Ultimately, the team approached the problem through the use of an inertial measurement unit (IMU), and the development of a graphical user interface to track the orientation of an US probe. The results supported that feedback regarding probe orientation can increase the ability to reproduce ultrasound images

Ultrasound Orientation Sensor

Ultrasound (US) is a painless method of gaining a visual representation of the internal structures of a human body. It is used to look for diseases and other abnormalities. In effort to minimize and eliminate the amount of error generated by the operation of an US machine, a team of WPI students conducted research into the causes and reasons as to why these problems are not resolved. Ultimately, the team approached the problem through the use of an inertial measurement unit (IMU), and the development of a graphical user interface to track the orientation of an US probe. The results supported that feedback regarding probe orientation can increase the ability to reproduce ultrasound images

Flexible Carbon-Based Electronics and Sensorized Neuroprosthesis

In the United States alone, there are more than 2 million people living with limb loss and prosthetic devices have long been the solution to recover their activities of daily living. However, many of the prosthetic users reported their dissatisfaction with current prostheses and some even abandoned theirs due to poor comfort and limited performance. To improve prosthetic control, advancements in surgical interfaces and sensorized neuroprosthesis are two major focus and have seen great potential. Both perspectives are presented in this work. Several reinnervated muscle surgeries have been invented to enable a better communication with muscle and nerves and a stable interface is essential to record robust muscle signals which are utilized to control a neuroprosthesis. Each muscle target may have slightly different anatomy and the current state-of-the-art implantable electrodes are complex and not easily reproducible and customizable. To address this problem, I present a simple, rapid electrode fabrication method to record muscle signals and easy-to-use electrode materials using carbon black/polydimethylsiloxane (PDMS) composite. Acute in vivo testing shows that the electrodes are highly functional and have the potential to enable large-scale muscle signal recordings with extensive data to improve the neuroprosthetic control. In addition to novel neural interfaces, sensory perception is also critical to improve the manipulation of objects with a prosthesis and enhances prosthetic performance and embodiment with feedback to the user. With recent advances in tactile sensing technology and neuromorphic stimulation interface, efficient real-time communication and functioning between them are still missing. In this work, I build and test a closed-loop system that integrates tactile sensing and neuromorphic electrical stimulation. The system functions in real time and the parameters of the sensory stimulation through transcutaneous electrical nerve stimulation (TENS) convey temporal information and dynamically change responding to real-time tactile data

Development of highly sensitive multimodal tactile sensor

The sense of touch is crucial for interpreting exteroceptive stimuli, and for moderating physical interactions with one’s environment during object grasping and manipulation tasks. For years, tactile researchers have sought a method that will allow robots to achieve the same tactile sensing capabilities as humans, but the solution has remained elusive. This is a problem for people in the medical and robotics communities, as prosthetic and robotic limbs provide little or no force feedback during contact with objects. During object manipulation tasks, the inability to control the force (applied by the prosthetic or robotic hand to the object) frequently results in damage to the object. Moreover, amputees must compensate for the lack of tactility by paying continuous visual attention to the task at hand, making even the simplest task a frustrating and time-consuming endeavor. We believe that these challenges of object manipulation might best be addressed by a closed feedback loop with a tactile sensory system that is capable of detecting multiple stimuli. To this end, the goal of our research is the development of a tactile sensor that mimics the human sensory apparatus as closely as possible. Thus far, tactile sensors have been unable to match the human sensory apparatus in terms of simultaneous multimodality, high resolution, and broad sensitivity. In particular, previous sensors have typically been able to sense either a wide range of forces, or very low forces, but never both at the same time; and they are designed for either static or dynamic sensing, rather than multimodality. These restrictions have left them unsuited to the needs of robotic applications. Capacitance-based sensors represent the most promising approach, but they too must overcome many limitations. Although recent innovations in the touch screen industry have resolved the issue of processing complexity, through the replacement of clunky processing circuits with new integrated circuits (ICs), most capacitive sensors still remain limited by hysteresis and narrow ranges of sensitivity, due to the properties of their dielectrics. In this thesis, we present the design of a new capacitive tactile sensor that is capable of making highly accurate measurements at low force levels, while also being sensitive to a wide range of forces. Our sensor is not limited to the detection of either low forces or broad sensitivity, because the improved soft dielectric that we have constructed allows it to do both at the same time. To construct the base of the dielectric, we used a geometrically modified silicone material. To create this material, we used a soft-lithography process to construct microfeatures that enhance the silicone’s compressibility under pressure. Moreover, the silicone was doped with high-permittivity ceramic nanoparticles, thereby enhancing the capacitive response of the sensor. Our dielectric features a two-stage microstructure, which makes it very sensitive to low forces, while still able to measure a wide range of forces. Despite these steps, and the complexity of the dielectric’s structure, we were still able to fabricate the dielectric using a relatively simple process. In addition, our sensor is not limited to either static or dynamic sensing; unlike previous sensors, it is capable of doing both simultaneously. This multimodality allows our sensor to detect fluctuating forces, even at very low force levels. Whereas past researchers have used separate technologies for static and dynamic sensing, our dynamic sensing unit is formed with same capacitive technology as the static one. This was possible because of the high sensitivity of our dielectric. We used the entire surface area effectively, by integrating the single dynamic sensing taxel on the same layer as the static sensing taxels. Essentially, the dynamic taxel takes the shape of the lines of a grid, filling in the spaces between the individual static taxels. For further optimization, the geometry of the dynamic taxel has been redesigned by fringing miniature traces of the dynamic taxel within the static taxels. In this way, the entire surface of the sensor is sensitive to both dynamic and static events. While this design slightly reduces the area that is covered by the static taxels, the trade-off is justified, as the capacitive behavior is boosted by the edge effect of the capacitor. The fusion of an innovative dielectric with a capacitive sensing IC has produced a highly sensitive tactile sensor that meets our goals regarding resolution, noise immunity, and overall performance. It is sensitive to forces ranging from 1 mN to 15 N. We verified the functionality of our sensor by mounting it on several of the most popular mechanical hands. Our grasp assessment experiments delivered promising results, and showed how our sensor might be further refined so that it can be used to accurately estimate the outcome of an attempted grasp. In future, we believe that combining an advanced robotic hand with the sensor we have developed will allow us to meet the demand for human-like tactile sensing abilities

Acoustic-based Smart Tactile Sensing in Social Robots

Mención Internacional en el título de doctorEl sentido del tacto es un componente crucial de la interacción social humana y es único entre los cinco sentidos. Como único sentido proximal, el tacto requiere un contacto físico cercano o directo para registrar la información. Este hecho convierte al tacto en una modalidad de interacción llena de posibilidades en cuanto a comunicación social. A través del tacto, podemos conocer la intención de la otra persona y comunicar emociones. De esta idea surge el concepto de social touch o tacto social como el acto de tocar a otra persona en un contexto social. Puede servir para diversos fines, como saludar, mostrar afecto, persuadir y regular el bienestar emocional y físico. Recientemente, el número de personas que interactúan con sistemas y agentes artificiales ha aumentado, principalmente debido al auge de los dispositivos tecnológicos, como los smartphones o los altavoces inteligentes. A pesar del auge de estos dispositivos, sus capacidades de interacción son limitadas. Para paliar este problema, los recientes avances en robótica social han mejorado las posibilidades de interacción para que los agentes funcionen de forma más fluida y sean más útiles. En este sentido, los robots sociales están diseñados para facilitar interacciones naturales entre humanos y agentes artificiales. El sentido del tacto en este contexto se revela como un vehículo natural que puede mejorar la Human-Robot Interaction (HRI) debido a su relevancia comunicativa en entornos sociales. Además de esto, para un robot social, la relación entre el tacto social y su aspecto es directa, al disponer de un cuerpo físico para aplicar o recibir toques. Desde un punto de vista técnico, los sistemas de detección táctil han sido objeto recientemente de nuevas investigaciones, sobre todo dedicado a comprender este sentido para crear sistemas inteligentes que puedan mejorar la vida de las personas. En este punto, los robots sociales se han convertido en dispositivos muy populares que incluyen tecnologías para la detección táctil. Esto está motivado por el hecho de que un robot puede esperada o inesperadamente tener contacto físico con una persona, lo que puede mejorar o interferir en la ejecución de sus comportamientos. Por tanto, el sentido del tacto se antoja necesario para el desarrollo de aplicaciones robóticas. Algunos métodos incluyen el reconocimiento de gestos táctiles, aunque a menudo exigen importantes despliegues de hardware que requieren de múltiples sensores. Además, la fiabilidad de estas tecnologías de detección es limitada, ya que la mayoría de ellas siguen teniendo problemas tales como falsos positivos o tasas de reconocimiento bajas. La detección acústica, en este sentido, puede proporcionar un conjunto de características capaces de paliar las deficiencias anteriores. A pesar de que se trata de una tecnología utilizada en diversos campos de investigación, aún no se ha integrado en la interacción táctil entre humanos y robots. Por ello, en este trabajo proponemos el sistema Acoustic Touch Recognition (ATR), un sistema inteligente de detección táctil (smart tactile sensing system) basado en la detección acústica y diseñado para mejorar la interacción social humano-robot. Nuestro sistema está desarrollado para clasificar gestos táctiles y localizar su origen. Además de esto, se ha integrado en plataformas robóticas sociales y se ha probado en aplicaciones reales con éxito. Nuestra propuesta se ha enfocado desde dos puntos de vista: uno técnico y otro relacionado con el tacto social. Por un lado, la propuesta tiene una motivación técnica centrada en conseguir un sistema táctil rentable, modular y portátil. Para ello, en este trabajo se ha explorado el campo de las tecnologías de detección táctil, los sistemas inteligentes de detección táctil y su aplicación en HRI. Por otro lado, parte de la investigación se centra en el impacto afectivo del tacto social durante la interacción humano-robot, lo que ha dado lugar a dos estudios que exploran esta idea.The sense of touch is a crucial component of human social interaction and is unique among the five senses. As the only proximal sense, touch requires close or direct physical contact to register information. This fact makes touch an interaction modality full of possibilities regarding social communication. Through touch, we are able to ascertain the other person’s intention and communicate emotions. From this idea emerges the concept of social touch as the act of touching another person in a social context. It can serve various purposes, such as greeting, showing affection, persuasion, and regulating emotional and physical well-being. Recently, the number of people interacting with artificial systems and agents has increased, mainly due to the rise of technological devices, such as smartphones or smart speakers. Still, these devices are limited in their interaction capabilities. To deal with this issue, recent developments in social robotics have improved the interaction possibilities to make agents more seamless and useful. In this sense, social robots are designed to facilitate natural interactions between humans and artificial agents. In this context, the sense of touch is revealed as a natural interaction vehicle that can improve HRI due to its communicative relevance. Moreover, for a social robot, the relationship between social touch and its embodiment is direct, having a physical body to apply or receive touches. From a technical standpoint, tactile sensing systems have recently been the subject of further research, mostly devoted to comprehending this sense to create intelligent systems that can improve people’s lives. Currently, social robots are popular devices that include technologies for touch sensing. This is motivated by the fact that robots may encounter expected or unexpected physical contact with humans, which can either enhance or interfere with the execution of their behaviours. There is, therefore, a need to detect human touch in robot applications. Some methods even include touch-gesture recognition, although they often require significant hardware deployments primarily that require multiple sensors. Additionally, the dependability of those sensing technologies is constrained because the majority of them still struggle with issues like false positives or poor recognition rates. Acoustic sensing, in this sense, can provide a set of features that can alleviate the aforementioned shortcomings. Even though it is a technology that has been utilised in various research fields, it has yet to be integrated into human-robot touch interaction. Therefore, in thiswork,we propose theATRsystem, a smart tactile sensing system based on acoustic sensing designed to improve human-robot social interaction. Our system is developed to classify touch gestures and locate their source. It is also integrated into real social robotic platforms and tested in real-world applications. Our proposal is approached from two standpoints, one technical and the other related to social touch. Firstly, the technical motivation of thiswork centred on achieving a cost-efficient, modular and portable tactile system. For that, we explore the fields of touch sensing technologies, smart tactile sensing systems and their application in HRI. On the other hand, part of the research is centred around the affective impact of touch during human-robot interaction, resulting in two studies exploring this idea.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Pedro Manuel Urbano de Almeida Lima.- Secretaria: María Dolores Blanco Rojas.- Vocal: Antonio Fernández Caballer