UN EJEMPLO DE INTEGRACIÓN REGIONAL Movilidad e Intercambio

Las Universidades Nacionales del NOA, desde las Unidades Académicas que ofrecen carreras de Ingeniería, elaboraron propuestas con el objeto de promover la constitución de ámbitos de reflexión y planificación sobre articulación y flexibilización de planes de estudios, en vista a la integración del sistema de Educación Superior; logrando: diseñar un Ciclo Común Articulado (CCA) para la familia de Carreras de Ingeniería, ejecutar acciones para su efectiva puesta en marcha y, dar a conocer a otras Universidades Nacionales promoviendo su incorporación en ellas. De este modo, se buscó favorecer la movilidad de estudiantes entre las Universidades participantes y estimular el desarrollo de innovaciones académicas y de gestión, potenciando las fortalezas que poseen. El diseño, adecuado a veintidós carreras, ha sido organizado a partir de áreas curriculares con contenidos y bibliografía básica comunes y rangos de cargas horarias. Este proceso innovador e inédito para la región, ha necesitado del desarrollo de normativas específicas para garantizar que los estudios realizados por los alumnos en otra Universidad del consorcio, tenga un reconocimiento académico en la Universidad receptora, ha permitido impulsar modelos y ha favorecido la participación en los ciclos generales de conocimientos básicos con actividades que serían mejoradas con el uso de las NTIyCs.

SLAM algorithm applied to robotics assistance for navigation in unknown environments

<p>Abstract</p> <p>Background</p> <p>The combination of robotic tools with assistance technology determines a slightly explored area of applications and advantages for disability or elder people in their daily tasks. Autonomous motorized wheelchair navigation inside an environment, behaviour based control of orthopaedic arms or user's preference learning from a friendly interface are some examples of this new field. In this paper, a Simultaneous Localization and Mapping (SLAM) algorithm is implemented to allow the environmental learning by a mobile robot while its navigation is governed by electromyographic signals. The entire system is part autonomous and part user-decision dependent (semi-autonomous). The environmental learning executed by the SLAM algorithm and the low level behaviour-based reactions of the mobile robot are robotic autonomous tasks, whereas the mobile robot navigation inside an environment is commanded by a Muscle-Computer Interface (MCI).</p> <p>Methods</p> <p>In this paper, a sequential Extended Kalman Filter (EKF) feature-based SLAM algorithm is implemented. The features correspond to lines and corners -concave and convex- of the environment. From the SLAM architecture, a global metric map of the environment is derived. The electromyographic signals that command the robot's movements can be adapted to the patient's disabilities. For mobile robot navigation purposes, five commands were obtained from the MCI: turn to the left, turn to the right, stop, start and exit. A kinematic controller to control the mobile robot was implemented. A low level behavior strategy was also implemented to avoid robot's collisions with the environment and moving agents.</p> <p>Results</p> <p>The entire system was tested in a population of seven volunteers: three elder, two below-elbow amputees and two young normally limbed patients. The experiments were performed within a closed low dynamic environment. Subjects took an average time of 35 minutes to navigate the environment and to learn how to use the MCI. The SLAM results have shown a consistent reconstruction of the environment. The obtained map was stored inside the Muscle-Computer Interface.</p> <p>Conclusions</p> <p>The integration of a highly demanding processing algorithm (SLAM) with a MCI and the communication between both in real time have shown to be consistent and successful. The metric map generated by the mobile robot would allow possible future autonomous navigation without direct control of the user, whose function could be relegated to choose robot destinations. Also, the mobile robot shares the same kinematic model of a motorized wheelchair. This advantage can be exploited for wheelchair autonomous navigation.</p

Algoritmo de SLAM en un robot móvil gobernado por una interface cerebro-computadora

En este trabajo se presenta la aplicación de un algoritmo de Localización yMapeo Simultáneos (SLAM, por sus siglas en inglés de Simultaneous Localization andMapping) en una Interface Cerebro-Computadora (ICC) que gobierna la navegación de unrobot móvil. La ICC consta de un panel con lugares y funciones predefinidas dentro de unambiente conocido. El paciente, mediante sus señales electroencefálicas, puede elegir avoluntad desde el panel de control, la función a ejecutar o el destino a alcanzar por el robotmóvil. El algoritmo de SLAM permite generar mapas de nuevos entornos. Estos mapas, sonsegmentados y adicionados a la ICC, ampliando así las opciones del panel. Con los mapasobtenidos es posible generar trayectorias de navegación para el robot móvil. Acompañaneste trabajo, los resultados experimentales obtenido

Effects and cost of different strategies to eliminate hepatitis C virus transmission in Pakistan: a modelling analysis

Background The WHO elimination strategy for hepatitis C virus advocates scaling up screening and treatment to reduce global hepatitis C incidence by 80% by 2030, but little is known about how this reduction could be achieved and the costs of doing so. We aimed to evaluate the effects and cost of different strategies to scale up screening and treatment of hepatitis C in Pakistan and determine what is required to meet WHO elimination targets for incidence. Methods We adapted a previous model of hepatitis C virus transmission, treatment, and disease progression for Pakistan, calibrating using available data to incorporate a detailed cascade of care for hepatitis C with cost data on diagnostics and hepatitis C treatment. We modelled the effect on various outcomes and costs of alternative scenarios for scaling up screening and hepatitis C treatment in 2018–30. We calibrated the model to country-level demographic data for 1960–2015 (including population growth) and to hepatitis C seroprevalence data from a national survey in 2007–08, surveys among people who inject drugs (PWID), and hepatitis C seroprevalence trends among blood donors. The cascade of care in our model begins with diagnosis of hepatitis C infection through antibody screening and RNA confirmation. Diagnosed individuals are then referred to care and started on treatment, which can result in a sustained virological response (effective cure). We report the median and 95% uncertainty interval (UI) from 1151 modelled runs. Findings One-time screening of 90% of the 2018 population by 2030, with 80% referral to treatment, was projected to lead to 13·8 million (95% UI 13·4–14·1) individuals being screened and 350 000 (315 000–385 000) treatments started annually, decreasing hepatitis C incidence by 26·5% (22·5–30·7) over 2018–30. Prioritised screening of high prevalence groups (PWID and adults aged ≥30 years) and rescreening (annually for PWID, otherwise every 10 years) are likely to increase the number screened and treated by 46·8% and decrease incidence by 50·8% (95% UI 46·1–55·0). Decreasing hepatitis C incidence by 80% is estimated to require a doubling of the primary screening rate, increasing referral to 90%, rescreening the general population every 5 years, and re-engaging those lost to follow-up every 5 years. This approach could cost US$8·1 billion, reducing to$3·9 billion with lowest costs for diagnostic tests and drugs, including health-care savings, and implementing a simplified treatment algorithm. Interpretation Pakistan will need to invest about 9·0% of its yearly health expenditure to enable sufficient scale up in screening and treatment to achieve the WHO hepatitis C elimination target of an 80% reduction in incidence by 2030. Funding UNITAID

Optimized EIF-SLAM algorithm for precision agriculture mapping based on stems detection

Precision agricultural maps are required for agricultural machinery navigation, path planning and plantation supervision. In this work we present a Simultaneous Localization and Mapping (SLAM) algorithm solved by an Extended Information Filter (EIF) for agricultural environments (olive groves). The SLAM algorithm is implemented on an unmanned non-holonomic car-like mobile robot. The map of the environment is based on the detection of olive stems from the plantation. The olive stems are acquired by means of both: a range sensor laser and a monocular vision system. A support vector machine (SVM) is implemented on the vision system to detect olive stems on the images acquired from the environment. Also, the SLAM algorithm has an optimization criterion associated with it. This optimization criterion is based on the correction of the SLAM system state vector using only the most meaningful stems - from an estimation convergence perspective - extracted from the environment information without compromising the estimation consistency. The optimization criterion, its demonstration and experimental results within real agricultural environments showing the performance of our proposal are also included in this work.Fil: Auat Cheein, F.. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Steiner, G.. Universidad Tecnológica Nacional; ArgentinaFil: Perez Paina, G.. Universidad Tecnológica Nacional; ArgentinaFil: Carelli Albarracin, Ricardo Oscar. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

A modular sensing system with CANBUS communication for assisted navigation of an agricultural mobile robot

Autonomous navigation of mobile robots inside unstructured agricultural fields proposes serious challenges due to the extreme variations in high-density bushes, the presence of random obstacles, and the inaccuracies in the GPS and IMU measurements. Advanced perception solutions are therefore required to assist the existing GPS-based navigation and to improve the reliability of the operation. This paper reports on the development and evaluation of a modular and scalable sensing system to assist the autonomous navigation of an agricultural mobile robot by providing it with collision avoidance capabilities. The robot benefited from a four-wheel steering mechanism that could be driven remotely via a 2.4 GHz wireless transmitter and could be programmed using the Robot Operating System (ROS) to follow waypoints. Multiple arrays of Time-of-Flight and infrared sensors with independent processing units were installed on the left, right, and front of the robot to enable a distributed control system. Communication between the sensor modules was realized via a CAN network. The collision avoidance system then exchanged messages with the robot computer over Ethernet using ROS on multiple machines scheme. A virtual model of the robot with an exact sensing setup was replicated in a robotic simulator to accelerate experimenting with different control algorithms and to optimize the sensors’ functionality. The simulation scenes and dynamic models were then improved by manually driving the robot in a real berry field for collecting sensor and steering data. Results from the simulation showed that the robot was able to autonomously navigate in different tracks and stabilize itself in the presence of random obstacles using a fuzzy knowledge-based algorithm. Preliminary field tests suggested that the Exponential filter was necessary to be implemented on each sensor for removing noise and outliers. The proposed approach created a flexible framework for exchanging data between each of the sensor ECUs and preventing the robot from colliding with random obstacles in front, left, and right. The study confirmed the functionality of the affordable sensing system and control architecture and can be suggested as an alternative solution for the high-end 3D LiDARs and the complex simultaneous localization and mapping methods

A modular sensing system with CANBUS communication for assisted navigation of an agricultural mobile robot

Autonomous navigation of mobile robots inside unstructured agricultural fields proposes serious challenges due to the extreme variations in high-density bushes, the presence of random obstacles, and the inaccuracies in the GPS and IMU measurements. Advanced perception solutions are therefore required to assist the existing GPS-based navigation and to improve the reliability of the operation. This paper reports on the development and evaluation of a modular and scalable sensing system to assist the autonomous navigation of an agricultural mobile robot by providing it with collision avoidance capabilities. The robot benefited from a four-wheel steering mechanism that could be driven remotely via a 2.4 GHz wireless transmitter and could be programmed using the Robot Operating System (ROS) to follow waypoints. Multiple arrays of Time-of-Flight and infrared sensors with independent processing units were installed on the left, right, and front of the robot to enable a distributed control system. Communication between the sensor modules was realized via a CAN network. The collision avoidance system then exchanged messages with the robot computer over Ethernet using ROS on multiple machines scheme. A virtual model of the robot with an exact sensing setup was replicated in a robotic simulator to accelerate experimenting with different control algorithms and to optimize the sensors’ functionality. The simulation scenes and dynamic models were then improved by manually driving the robot in a real berry field for collecting sensor and steering data. Results from the simulation showed that the robot was able to autonomously navigate in different tracks and stabilize itself in the presence of random obstacles using a fuzzy knowledge-based algorithm. Preliminary field tests suggested that the Exponential filter was necessary to be implemented on each sensor for removing noise and outliers. The proposed approach created a flexible framework for exchanging data between each of the sensor ECUs and preventing the robot from colliding with random obstacles in front, left, and right. The study confirmed the functionality of the affordable sensing system and control architecture and can be suggested as an alternative solution for the high-end 3D LiDARs and the complex simultaneous localization and mapping methods

Internet of robotic things with a local LoRa network for teleoperation of an agricultural mobile robot using a digital shadow

In unstructured agricultural fields where autonomous navigation is challenging and demands additional safety, the operator’s experience and knowledge are essential for supervising operations and making decisions beyond the robot’s autonomous capabilities. Local networks with long-range wireless communication combined with digital twin concepts are promising solutions that can be used for robot teleoperation. The purpose of this study was to demonstrate the feasibility of supervising a mobile robot inside berry orchards using a digital shadow from a long-range distance (between 300 and 3000 m), with the primary objective of assisting the robot in navigating in complex situations such as row-end turning. This involved creating a virtual representation of the robot that mirrors its state and actions, allowing the remote operator to monitor and guide the robot effectively. The system comprised a GPS-based navigation controller with collision avoidance sensors, two sets of LoRa transmitters and repeaters, a simulation environment with a digital shadow of the robot, and a graphical user interface for the remote operator. Information about the digital shadow’s state, including location, orientation, and distances to obstacles, was received as a message by the LoRa gateway and was used to update the path for the actual robot that interfaced with the Robot Operating System (ROS). The main research hypothesis aimed to test the quality of the LoRa communication link between the robot and the operator, as well as the robustness of the robot’s control system, with an emphasis on the architecture, communication link, and situation awareness creation. Preliminary results showed that depending on the environment, the average packet loss was 12% at distances of approximately 2300 m. Our results highlight some of the core technical challenges that need to be addressed for an effective teleoperation system, including latency, stability, and the limited range of wireless communication. Future works involves evaluating the performance and reliability of the proposed method under different field conditions and scenarios, as well as considering the use of the 5G network for a significant improvement in data transmission speed, navigation efficiency, and visual feedback. Upon successful implementation, this study has the potential to enhance the efficiency and safety of robot navigation, providing a practical solution for remote supervision in challenging environments