746 research outputs found
Reinforcement Learning with Frontier-Based Exploration via Autonomous Environment
Active Simultaneous Localisation and Mapping (SLAM) is a critical problem in
autonomous robotics, enabling robots to navigate to new regions while building
an accurate model of their surroundings. Visual SLAM is a popular technique
that uses virtual elements to enhance the experience. However, existing
frontier-based exploration strategies can lead to a non-optimal path in
scenarios where there are multiple frontiers with similar distance. This issue
can impact the efficiency and accuracy of Visual SLAM, which is crucial for a
wide range of robotic applications, such as search and rescue, exploration, and
mapping. To address this issue, this research combines both an existing
Visual-Graph SLAM known as ExploreORB with reinforcement learning. The proposed
algorithm allows the robot to learn and optimize exploration routes through a
reward-based system to create an accurate map of the environment with proper
frontier selection. Frontier-based exploration is used to detect unexplored
areas, while reinforcement learning optimizes the robot's movement by assigning
rewards for optimal frontier points. Graph SLAM is then used to integrate the
robot's sensory data and build an accurate map of the environment. The proposed
algorithm aims to improve the efficiency and accuracy of ExploreORB by
optimizing the exploration process of frontiers to build a more accurate map.
To evaluate the effectiveness of the proposed approach, experiments will be
conducted in various virtual environments using Gazebo, a robot simulation
software. Results of these experiments will be compared with existing methods
to demonstrate the potential of the proposed approach as an optimal solution
for SLAM in autonomous robotics.Comment: 23 pages, Journa
Laser-Based Detection and Tracking of Moving Obstacles to Improve Perception of Unmanned Ground Vehicles
El objetivo de esta tesis es desarrollar un sistema que mejore la etapa de percepción de vehículos terrestres no tripulados (UGVs) heterogéneos, consiguiendo con ello una navegación robusta en términos de seguridad y ahorro energético en diferentes entornos reales, tanto interiores como exteriores. La percepción debe tratar con obstáculos estáticos y dinámicos empleando sensores heterogéneos, tales como, odometría, sensor de distancia láser (LIDAR), unidad de medida inercial (IMU) y sistema de posicionamiento global (GPS), para obtener la información del entorno con la precisión más alta, permitiendo mejorar las etapas de planificación y evitación de obstáculos.
Para conseguir este objetivo, se propone una etapa de mapeado de obstáculos dinámicos (DOMap) que contiene la información de los obstáculos estáticos y dinámicos. La propuesta se basa en una extensión del filtro de ocupación bayesiana (BOF) incluyendo velocidades no discretizadas. La detección de velocidades se obtiene con Flujo Óptico sobre una rejilla de medidas LIDAR discretizadas. Además, se gestionan las oclusiones entre obstáculos y se añade una etapa de seguimiento multi-hipótesis, mejorando la robustez de la propuesta (iDOMap).
La propuesta ha sido probada en entornos simulados y reales con diferentes plataformas robóticas, incluyendo plataformas comerciales y la plataforma (PROPINA) desarrollada en esta tesis para mejorar la colaboración entre equipos de humanos y robots dentro del proyecto ABSYNTHE. Finalmente, se han propuesto métodos para calibrar la posición del LIDAR y mejorar la odometría con una IMU
Radar-based Feature Design and Multiclass Classification for Road User Recognition
The classification of individual traffic participants is a complex task,
especially for challenging scenarios with multiple road users or under bad
weather conditions. Radar sensors provide an - with respect to well established
camera systems - orthogonal way of measuring such scenes. In order to gain
accurate classification results, 50 different features are extracted from the
measurement data and tested on their performance. From these features a
suitable subset is chosen and passed to random forest and long short-term
memory (LSTM) classifiers to obtain class predictions for the radar input.
Moreover, it is shown why data imbalance is an inherent problem in automotive
radar classification when the dataset is not sufficiently large. To overcome
this issue, classifier binarization is used among other techniques in order to
better account for underrepresented classes. A new method to couple the
resulting probabilities is proposed and compared to others with great success.
Final results show substantial improvements when compared to ordinary
multiclass classificationComment: 8 pages, 6 figure
On-plate autonomous exploration for an inspection robot using ultrasonic guided waves
Autonomous Robotic Exploration is a major research issue in robotics incorporating the
aspect of how to make decisions for the next actions to maximize information gain and minimize costs. In this work, we elaborate an active-sensing strategy based on frontier-based
exploration to enable the autonomous reconstruction of the geometry of a metal surface by
a mobile robot relying on ultrasonic echoes. Such a strategy can be beneficial to the development of a fully autonomous robotic agent for the inspection of large metal structures
such as storage tanks and ship hulls. Our exploration strategy relies on the occupancy grid
generated by detecting the first echo of the signal referring to the closest edge to the sensor,
and it employs a utility function that we define to balance travel cost and information gain
using the plate’s geometry estimation. Next, the sensor is directed to the next best location.
In simulation, the method developed is evaluated and compared with multiple algorithms,
essentially closest and random frontier point selection. Finally, an experiment using a mobile robot equipped with co-localized emitter/receiver pair of transducers is used to validate
the viability of the proposed approach.M.S
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