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.