Un nuevo concepto en escáner adaptativo de bajo costo para robots móviles

El problema fundamental en las aplicaciones de robots móviles, es la necesidad de conocer con exactitud la posición del vehículo, para poder localizarse en el espacio y evitar obstáculos en su camino. En la búsqueda de una solución, los investigadores e ingenieros han desarrollado diferentes sensores, sistemas y técnicas.Los robots móviles modernos se basan en la información obtenida de diferentes sensores y en sofisticados algoritmos de fusión de datos. Por tal razón, en este artículo se propone un nuevo concepto de escáner de adaptación a bajo costo, basándose en patrones de luz proyectados. La ventaja principal del sistema propuesto es: su adaptabilidad, que permite en los robots el escaneo rápido de los alrededores durante la búsqueda de obstáculos y una exploración más detallada de un objeto determinado, para poder recu-perar así, su configuración de la superficie y realizar algunos análisis limitados.El artículo aborda el concepto de un escáner de este tipo, donde se logró la prueba del concepto utilizando un proyector de oficina DLP. Durante las mediciones, la exactitud del sistema propuesto se puso a prueba, usando obstáculos con objetos de configuraciones conocidas. De esta manera, los resultados obtenidos son presentados, analizados y se discuten las conclusiones sobre el desempeño del sistema para generar posibles mejoramientos.A fundamental problem in mobile robot applications is the need for accurate knowledge of the position of a vehicle for localizing itself and for avoiding obstacles in its path. In the search for a solution to this problem, researchers and engineers have developed different sensors, systems and techniques. Modern mobile robots relay information obtained from a variety of sensors and sophisticated data fusion algorithms. In this paper, a novel concept for a low-cost adaptive scanner based on a projected light pattern is proposed. The main advantage of the proposed system is its adaptivity, which enables the rapid scanning of the robot’s surroundings in search of obstacles and a more detailed scan of a single object to retrieve its surface configuration and perform some limited analyses. This paper addresses the concept behind such a scanner, where a proof-of-concept is achieved using an office DLP projector. During the measurements, the accuracy of the proposed system was tested on obstacles and objects with known configurations. The obtained results are presented and analyzed, and conclusions about the system’s performance and possible improvements are discussed

A Novel Low-Cost Adaptive Scanner Concept for Mobile Robots

A fundamental problem in mobile robot applications is the need for accurate knowledge of the position of a vehicle for localizing itself and for avoiding obstacles in its path. In the search for a solution to this problem, researchers and engineers have developed different sensors, systems and techniques. Modern mobile robots relay information obtained from a variety of sensors and sophisticated data fusion algorithms. In this paper, a novel concept for a low-cost adaptive scanner based on a projected light pattern is proposed. The main advantage of the proposed system is its adaptivity, which enables the rapid scanning of the robot’s surroundings in search of obstacles and a more detailed scan of a single object to retrieve its surface configuration and perform some limited analyses. This paper addresses the concept behind such a scanner, where a proof-of-concept is achieved using an office DLP projector. During the measurements, the accuracy of the proposed system was tested on obstacles and objects with known configurations. The obtained results are presented and analyzed, and conclusions about the system’s performance and possible improvements are discussed

A Novel Low-Cost Adaptive Scanner Concept for Mobile Robots

<p class="Abstractandkeywordscontent">A fundamental problem in mobile robot applications is the need for accurate knowledge of the position of a vehicle for localizing itself and for avoiding obstacles in its path. In the search for a solution to this problem, researchers and engineers have developed different sensors, systems and techniques. Modern mobile robots relay information obtained from a variety of sensors and sophisticated data fusion algorithms. In this paper, a novel concept for a low-cost adaptive scanner based on a projected light pattern is proposed. The main advantage of the proposed system is its adaptivity, which enables the rapid scanning of the robot’s surroundings in search of obstacles and a more detailed scan of a single object to retrieve its surface configuration and perform some limited analyses. This paper addresses the concept behind such a scanner, where a proof-of-concept is achieved using an office DLP projector. During the measurements, the accuracy of the proposed system was tested on obstacles and objects with known configurations. The obtained results are presented and analyzed, and conclusions about the system’s performance and possible improvements are discussed.</p

Towards Real-Time Machine Learning Based Signal/Background Selection in the CMS Detector Using Quantized Neural Networks and Input Data Reduction

To boost its discovery potential, the Large Hadron Collider (LHC) is being prepared for an extensive upgrade. The new phase, High Luminosity LHC (HL-LHC), will operate at luminosity (number proportional to the rate of collisions) increased by a factor of five. Such an increase in luminosity consequently will result in enormous amounts of generated data, the vast majority of which is uninteresting data or pile up (PU). HL-LHC detectors, including Compact Muon Solenoid (CMS), will thus have to rely on innovative technologies and methods to select, collect and analyze collisions data. In charge of data reduction at the early stages of data streaming is a Level 1 Trigger (L1T), the real-time event selection system based on information from calorimeters and muon detectors, with a decision time of around 12 microseconds. For the L1T method, we propose quantized neural network models deployed in targeted L1T devices, Field Programmable Gate Arrays (FPGAs), as a classifier between electromagnetic and pile-up/QCD showers. Traditional classifiers are based on cluster shapes and hand-crafted features, while the proposed quantized neural network uses raw detector data, that speeds up the classification process. Data reduction using selection and quantization additionally decreases model size retaining accuracy. The model execution requires less than 1 microsecond, making it a possible mechanism for real-time signal/background classification