The integration of robotics and mobile networks (5G/6G) through the Internet of Robotic Things (IoRT) is revolutionizing telemedicine, enabling remote physician participation in scenarios where specialists are scarce, where there is a high risk to them, such as in conflicts or natural disasters, or where access to a medical facility is not possible. Nevertheless, touching a human safely with a robotic arm in non-engineered or even out-of-hospital environments presents substantial challenges. This article presents a novel IoRT approach for healthcare in or from remote areas, enabling interaction between a specialist’s hand and a robotic hand. We introduce the IoRT-in-hand: a smart, lightweight end-effector that extends the specialist’s hand, integrating a medical instrument, an RGB camera with servos, a force/torque sensor, and a mini-PC with Internet connectivity. Additionally, we propose an open-source Android app combining MQTT and ROS for real-time remote manipulation, alongside an Edge–Cloud architecture that links the physical robot with its Digital Twin (DT), enabling precise control and 3D visual feedback of the robot’s environment. A proof of concept is presented for the proposed tele-robotic system, using a 6-DOF manipulator with the IoRT-in-hand to perform an ultrasound scan. Teleoperation was conducted over 2300 km via a 5G NSA network on the operator side and a wired network in a laboratory on the robot side. Performance was assessed through human subject feedback, sensory data, and latency measurements, demonstrating the system’s potential for remote healthcare and emergency applications. The source code and CAD models of the IoRT-in-hand prototype are publicly available in an open-access repository to encourage reproducibility and facilitate further developments in robotic telemedicine.

La exploración en entornos de catástrofes proporciona información valiosa de alta fidelidad al personal de los dispositivos de rescate ante situaciones de desastre, ofreciendo la posibilidad de reducir el tiempo de búsqueda y recuperación de vı́ctimas. Este trabajo presenta un método de exploración que evalúa las fronteras del entorno conocido basado en la entropı́a mediante una función de expectativa, con el objetivo de maximizar la ganancia de información. De esta forma el método propuesto emplea esta métrica para planificar la exploración en base a la expectativa de ganancia de información futura, asegurando una estrategia que minimiza el tiempo de exploración al mismo tiempo que maximiza la incorporación de nueva información al mapa. Debido al enfoque empleado para resolver el problema se consigue evitar la dependencia del método de ganancia de información con los mapas de tamaño fijo, y se propone un modelo independiente del sensor utilizado en la exploración donde se considera la distribución de obstáculos en la cercanı́a de las fronteras. Para su evaluación, se presentan resultados en diferentes entornos con simulaciones que demuestran la mayor eficiencia en la planificación de la exploración de las zonas desconocidas hasta completar el conocimiento completo del entorno a explorar. El método propuesto está públicamente disponible.

El conjunto de datos se obtuvo utilizando una mano robótica de tres dedos: uno fijo con un sensor táctil de alta resolución y dos dedos subactuados con sensores propioceptivos., Conjunto de datos formados por información kinestesica y táctil obtenido de un agarre de antebrazo con una pinza.

Crazyflie 2.1 en un entorno controlado dentro del laboratorio Robotics and Mechatronics Lab (IMECH.UMA), Universidad de Málaga (España).

Las trayectorias fueron diseñadas mediante un planificador basado en el Principio del Máximo de Pontryagin y un modelo de punto masa (PMM), que genera soluciones de mínimo tiempo en múltiples waypoints. Dichas trayectorias se aproximaron mediante polinomios de séptimo orden para ser seguidas por el dron. Posteriormente, los vuelos se llevaron a cabo y se registraron usando cámaras OptiTrack PrimeX 13, generando datos en formato CSV con marcas de tiempo, posiciones, velocidades y aceleraciones en los tres ejes., Este dataset contiene trayectorias experimentales de un dron Crazyflie 2.1 en entornos indoor, obtenidas mediante un sistema de captura de movimiento OptiTrack. Incluye trayectorias de referencia planificadas con un modelo de punto masa (PMM), aproximaciones polinómicas y registros reales de vuelo en circuitos con forma de círculo y de ocho. Los datos permiten analizar y comparar la planificación y el seguimiento de trayectorias en experimentos de vuelo autónomo.

This package provides the DEM-AIA (Digital Elevation Map – Any-Angle Inclination-Aware) trajectory planner for unmanned ground vehicles (UGVs). The planner computes feasible and efficient trajectories over digital elevation models (DEMs), explicitly considering: - Terrain slopes (pitch and roll). - Vehicle dynamic and geometric constraints (speed, center of gravity, length, width, slope tolerance). - Any-angle variant of the A* algorithm for more realistic and shorter paths. - Optional heuristic search and node re-expansion. The distribution includes both synthetic and real DEMs, MATLAB source code, precompiled MEX binaries, and a test script., Metadata:
- Application domain: Off-road robotics, terrain-aware trajectory planning.
- Data type: Digital Elevation Models (DEMs).
- Format: MATLAB .mat.
- Spatial resolution: depends on the selected DEM.
- Units: meters (altitude and coordinates).
- Restrictions: Distributed MEX files only run on MATLAB R2021b (Windows x64).
Recompilation is required for other versions or platforms., Planning safe and effective trajectories for off-road unmanned ground vehicles (UGV) is a critical Artificial Intelligence (AI) challenge that can benefit from recent advances in digital elevation models (DEM) for readily capturing accurate terrain geometry. Considering path slopes is crucial to preserve stability and assess terrain traversability at feasible speeds to optimize travel time, which is highly dependent on direction (i.e., pitch and roll). In this article, we propose a new DEM-based asymmetric inclination-aware (DEM-AIA) trajectory planner for ground vehicles. The planner is an any-angle variant of the A algorithm that computes pitch and roll estimations for each segment crossing cell triangles in the line-of-sight. Furthermore, we define a non-linear velocity constraints function that integrates information about tip-over safety limitations, maximum uphill and downhill slopes for the vehicle, and asymmetric modulation of nominal flat-ground velocity for all pitch and roll combinations. The planner produces a time sub-optimal trajectory with feasible speed references for each segment crossing a cell triangle. Moreover, we provide an extensive experimental analysis of inclination-aware performance on simulated and real-world DEMs as well as a comparison with state-of-the-art path planners adapted to travel-time optimization. An executable version of the planner with parameterizable variations is publicly available.

Accurate victim localization in Remote, Outdoor, Unstructured, and Disaster (ROUD) scenarios remains a significant challenge due to limited infrastructure, complex terrains, and time constraints inherent in Search and Rescue (SAR) operations. This article introduces an innovative real-time positioning system that leverages Fine Time Measurement (FTM), a feature of the IEEE 802.11mc amendment, to detect WiFi-enabled devices typically carried by potential victims. The system integrates a Hybrid Wireless Sensor Network (H-WSN) composed of static and mobile anchors mounted on Uncrewed Ground and Aerial Vehicles (UGVs and UAVs), within a Robot Operating System (ROS)-based architecture. A centralized Feedback Information System (FIS) processes real-time RTT data from the field, executes a multilateration algorithm, and provides live geolocation updates to SAR coordinators. The system was validated during a large-scale SAR drill, successfully locating two types of victims—a semi-hidden surface victim and a buried victim—within a 2000m2 area in under 7 minutes, without any filtering or post-processing. The maximum positioning error was 22.87m for the buried victim and 17.14m for the surface one. The complete system, including source code, dataset, and a containerized environment, is openly available to support reproducibility and further research, highlighting the potential of robotic and wireless technologies to enhance disaster response through accurate, real-time localization in complex environments.