TeslaCharge: Smart Robotic Charger Driven by Impedance Control and Human Haptic Patterns

The growing demand for electric vehicles requires the development of automated car charging methods. At the moment, the process of charging an electric car is completely manual, and that requires physical effort to accomplish the task, which is not suitable for people with disabilities. Typically, the effort in the research is focused on detecting the position and orientation of the socket, which resulted in a relatively high accuracy, $\pm 5 \: mm$ and $\pm 10^o$. However, this accuracy is not enough to complete the charging process. In this work, we focus on designing a novel methodology for robust robotic plug-in and plug-out based on human haptics, to overcome the error in the position and orientation of the socket. Participants were invited to perform the charging task, and their cognitive capabilities were recognized by measuring the applied forces along with the movement of the charger. Three controllers were designed based on impedance control to mimic the human patterns of charging an electric car. The recorded data from humans were used to calibrate the parameters of the impedance controllers: inertia $M_d$, damping $D_d$, and stiffness $K_d$. A robotic validation was performed, where the designed controllers were applied to the robot UR10. Using the proposed controllers and the human kinesthetic data, it was possible to successfully automate the operation of charging an electric car.Comment: Accepted to the 21st IEEE International Conference on Advanced Robotics (ICAR 2023). IEEE copyrigh

HaptiCharger: Robotic Charging of Electric Vehicles Based on Human Haptic Patterns

The growing demand for electric vehicles requires the development of automated car charging methods. At the moment, the process of charging an electric car is completely manual, and that requires physical effort to accomplish the task, which is not suitable for people with disabilities. Typically, the effort in the automation of the charging task research is focused on detecting the position and orientation of the socket, which resulted in a relatively high accuracy, 5 mm, and 10 degrees. However, this accuracy is not enough to complete the charging process. In this work, we focus on designing a novel methodology for robust robotic plug-in and plug-out based on human haptics to overcome the error in the orientation of the socket. Participants were invited to perform the charging task, and their cognitive capabilities were recognized by measuring the applied forces along with the movements of the charger. Eventually, an algorithm was developed based on the human's best strategies to be applied to a robotic arm.Comment: Manuscript accepted to IEEE ROBIO 202

CobotTouch: AR-based Interface with Fingertip-worn Tactile Display for Immersive Operation/Control of Collaborative Robots

Complex robotic tasks require human collaboration to benefit from their high dexterity. Frequent human-robot interaction is mentally demanding and time-consuming. Intuitive and easy-to-use robot control interfaces reduce the negative influence on workers, especially inexperienced users. In this paper, we present CobotTouch, a novel intuitive robot control interface with fingertip haptic feedback. The proposed interface consists of a projected Graphical User Interface on the robotic arm to control the position of the robot end-effector based on gesture recognition, and a wearable haptic interface to deliver tactile feedback on the user's fingertips. We evaluated the user's perception of the designed tactile patterns presented by the haptic interface and the intuitiveness of the proposed system for robot control in a use case. The results revealed a high average recognition rate of 75.25\% for the tactile patterns. An average NASA Task Load Index (TLX) indicated small mental and temporal demands proving a high level of the intuitiveness of CobotTouch for interaction with collaborative robots.Comment: 12 pages, 11 figures, Accepted paper in AsiaHaptics 202

MorphoArms: Morphogenetic Teleoperation of Multimanual Robot

Nowadays, there are few unmanned aerial vehicles (UAVs) capable of flying, walking and grasping. A drone with all these functionalities can significantly improve its performance in complex tasks such as monitoring and exploring different types of terrain, and rescue operations. This paper presents MorphoArms, a novel system that consists of a morphogenetic chassis and a hand gesture recognition teleoperation system. The mechanics, electronics, control architecture, and walking behavior of the morphogenetic chassis are described. This robot is capable of walking and grasping objects using four robotic limbs. Robotic limbs with four degrees-of-freedom are used as pedipulators when walking and as manipulators when performing actions in the environment. The robot control system is implemented using teleoperation, where commands are given by hand gestures. A motion capture system is used to track the user's hands and to recognize their gestures. The method of controlling the robot was experimentally tested in a study involving 10 users. The evaluation included three questionnaires (NASA TLX, SUS, and UEQ). The results showed that the proposed system was more user-friendly than 56% of the systems, and it was rated above average in terms of attractiveness, stimulation, and novelty.Comment: IEEE International Conference on Automation Science and Engineering (CASE 2023), Cordis, New Zeland, 26-30 August, 2023, in prin

RecyGlide : A Forearm-worn Multi-modal Haptic Display aimed to Improve User VR Immersion Submission

© 2019 Copyright held by the owner/author(s). Haptic devices have been employed to immerse users in VR environments. In particular, hand and finger haptic devices have been deeply developed. However, this type of devices occludes hand detection for some tracking systems, or, for some other tracking systems, it is uncomfortable for the users to wear two different devices (haptic and tracking device) on both hands. We introduce RecyGlide, a novel wearable multimodal display located at the forearm. The RecyGlide is composed of inverted five-bar linkages with 2 degrees of freedom (DoF) and vibration motors (see Fig. 1.(a). The device provides multimodal tactile feedback such as slippage, force vector, pressure, and vibration. We tested the discrimination ability of monomodal and multimodal stimuli patterns on the forearm and confirmed that the multimodal patterns have higher recognition rate. This haptic device was used in VR applications, and we proved that it enhances VR experience and makes it more interactive

DogTouch: CNN-based Recognition of Surface Textures by Quadruped Robot with High Density Tactile Sensors

The ability to perform locomotion in various terrains is critical for legged robots. However, the robot has to have a better understanding of the surface it is walking on to perform robust locomotion on different terrains. Animals and humans are able to recognize the surface with the help of the tactile sensation on their feet. Although, the foot tactile sensation for legged robots has not been much explored. This paper presents research on a novel quadruped robot DogTouch with tactile sensing feet (TSF). TSF allows the recognition of different surface textures utilizing a tactile sensor and a convolutional neural network (CNN). The experimental results show a sufficient validation accuracy of 74.37\% for our trained CNN-based model, with the highest recognition for line patterns of 90\%. In the future, we plan to improve the prediction model by presenting surface samples with the various depths of patterns and applying advanced Deep Learning and Shallow learning models for surface recognition. Additionally, we propose a novel approach to navigation of quadruped and legged robots. We can arrange the tactile paving textured surface (similar that used for blind or visually impaired people). Thus, DogTouch will be capable of locomotion in unknown environment by just recognizing the specific tactile patterns which will indicate the straight path, left or right turn, pedestrian crossing, road, and etc. That will allow robust navigation regardless of lighting condition. Future quadruped robots equipped with visual and tactile perception system will be able to safely and intelligently navigate and interact in the unstructured indoor and outdoor environment.Comment: Accepted paper at IEEE Vehicular Technology Conference 2022 (IEEE VTC 2022), IEEE copyrigh

Red Nacional de Reconocedores de Suelos

Ponencia presentada en el XXVII Congreso Argentino de la Ciencia del Suelo. Corrientes, 13 al 16 de octubre de 2020Los relevamientos sistemáticos de suelos en Argentina comenzaron en la década de 1960, en el marco del Plan Mapa de Suelos. Dicho plan, desarrollado y liderado por el INTA, dio impulso a la formación de especialistas y a la producción de cartografía de suelos a diferentes escalas. Sin embargo, a partir del año 2000 las actividades se redujeron notablemente y gran parte de los equipos provinciales formados hasta ese momento se desarticularon. Desde entonces los relevamientos continuaron de manera aislada sólo en aquellas provincias donde se mantuvieron los grupos de trabajo. Este hecho condujo a que actualmente diferentes regiones del país no cuenten con información acerca de las propiedades y distribución de suelos a una escala adecuada para la toma de decisiones. En este contexto, en el 2018 se crea la Red Nacional de Reconocedores de Suelos (RNRS) que organiza las capacidades técnicas y operativas a nivel nacional para dar pronta respuesta a la creciente demanda de cartografía. Se trata de un equipo interinstitucional e interdisciplinario de especialistas distribuidos por todo el país, que realiza tareas de relevamiento, produce y difunde cartografía básica y utilitaria de suelos, ofrece capacitación y genera espacios de discusión y actualización metodológica. A la fecha, la RNRS ha relevado aproximadamente 760.000 ha en el sur de Córdoba, estimando completar durante el presente año el relevamiento del departamento Río Cuarto. Esta estrategia organizacional permitirá avanzar en el mapeo semidetallado de suelos en nuestro país, estableciendo vinculaciones sinérgicas entre profesionales de diferentes instituciones a fin de fortalecer y potenciar los equipos de trabajo en cada región. El motivo de esta contribución es presentar la RNRS, sus objetivos, avances a la fecha y desafíos a futuro, haciendo una breve revisión del estado actual de los relevamientos a escala semidetallada en nuestro país.Fil: Moretti, L.M. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Cerro Azul; Argentina.Fil: Rodríguez, D. M. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Investigación Suelos; Argentina.Fil: Schulz, G. A. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Investigación Suelos; Argentina.Fil: Kurtz, Ditmar B. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Corrientes; Argentina.Fil: Altamirano, D. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi; Argentina.Fil: Amin, S. Universidad Nacional de Río Cuarto; Argentina.Fil: Angelini, M. E. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Investigación Suelos; Argentina.Fil: Babelis, G. C. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Juan; Argentina.Fil: Becerra, Miguel Alejandro. Universidad Nacional de Río Cuarto; Argentina.Fil: Becerra, Miguel Alejandro. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina.Fil: Bedendo, D. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Paraná; Argentina.Fil: Boldrini, C. Instituto Nacional de Tecnología Agropecuaria (INTA). Agencia de Extensión Rural Río Cuarto; Argentina.Fil: Bongiovanni, M. Universidad Nacional de Río Cuarto; Argentina.Fil: Bozzer, C. Universidad Nacional de Río Cuarto; Argentina.Fil: Cabrera, S. Universidad Nacional de Río Cuarto; Argentina.Fil: Canale, A. Instituto Nacional de Tecnología Agropecuaria (INTA). Agencia de Extensión Rural Río Cuarto; Argentina.Fil: Chilano, Y. Universidad Nacional de Río Cuarto; Argentina.Fil: Cholaky, Carmen Gloria. Universidad Nacional de Río Cuarto; Argentina.Fil: Cisneros, José Manuel. Universidad Nacional de Río Cuarto; Argentina.Fil: Colazo, Juan Cruz. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Luis; Argentina.Fil: Corigliano, J. Universidad Nacional de Río Cuarto; Argentina.Fil: Degioanni, Américo José. Universidad Nacional de Río Cuarto; Argentina.Fil: de la Fuente, J. C. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Investigación Suelos; Argentina.Fil: Escobar, D. Ministerio de Agricultura, Ganadería y Pesca; Argentina.Fil: Faule, Lautaro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi; Argentina.Fil: Galarza, C. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Marcos Juárez; Argentina.Fil: González, J. Universidad Nacional de Río Cuarto; Argentina.Fil: Holzmann, Rosa de Lima. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Alto Valle; Argentina.Fil: Irigoin, J. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Investigación Suelos; Argentina.Fil: Lanfranco, M. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi; Argentina.Fil: Leon Giacossa, C. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rafaela; Argentina.Fil: Matteio, J. P. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Corrientes; Argentina.Fil: Márquez, C. Gobierno de la Provincia de Córdoba. Ministerio de Agricultura y Ganadería; Argentina.Fil: Marzari, R. Universidad Nacional de Río Cuarto; Argentina.Fil: Mattalia, M. L. Universidad Nacional de Río Cuarto; Argentina.Fil: Morales Poclava, M. C. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; Argentina.Fil: Muñoz, S. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Marcos Juárez; Argentina.Fil: Paladino, I. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria AMBA; Argentina.Fil: Parra, B. Universidad Nacional de Río Cuarto; Argentina.Fil: Pérez, M. Gobierno de la Provincia de Córdoba. Ministerio de Agricultura y Ganadería; Argentina.Fil: Pezzola, A. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; Argentina.Fil: Perucca, S. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Corrientes; Argentina.Fil: Porcel de Peralta, Ricardo Félix. Gobierno de la Provincia de Córdoba. Ministerio de Agricultura y Ganadería; Argentina.Fil: Renaudeau, S. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi; Argentina.Fil: Salustio, M. Instituto Nacional de Tecnología Agropecuaria (INTA). Agencia de Extensión Rural Río Cuarto; Argentina.Fil: Sapino, V. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rafaela; Argentina.Fil: Tenti Vuegen, L. M. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Investigación Suelos; Argentina.Fil: Tosolini, R. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rafaela; Argentina.Fil: Vicondo, Manuel Eduardo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi; Argentina.Fil: Vicondo, Manuel Eduardo. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina.Fil: Vizgarra, L. A. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Quimilí; Argentina.Fil: Ybarra, D. D. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Corrientes; Argentina.Fil: Winschel, C. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; Argentina.Fil: Zamora, Eduardo M. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi; Argentina