A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles

Deep learning is recently showing outstanding results for solving a wide variety of robotic tasks in the areas of perception, planning, localization, and control. Its excellent capabilities for learning representations from the complex data acquired in real environments make it extremely suitable for many kinds of autonomous robotic applications. In parallel, Unmanned Aerial Vehicles (UAVs) are currently being extensively applied for several types of civilian tasks in applications going from security, surveillance, and disaster rescue to parcel delivery or warehouse management. In this paper, a thorough review has been performed on recent reported uses and applications of deep learning for UAVs, including the most relevant developments as well as their performances and limitations. In addition, a detailed explanation of the main deep learning techniques is provided. We conclude with a description of the main challenges for the application of deep learning for UAV-based solutions

Aplicación de la metodología PCI en la evaluación del estado del pavimento flexible de la avenida metropolitana II de la ciudad de Trujillo

El presente trabajo de tesis, tiene por objetivo principal determinar el índice de condición del pavimento flexible en la Avenida Metropolitana II de la ciudad de Trujillo haciendo uso de la metodología PCI (Pavement Condition Index), es decir, se precisa el nivel de degradación que presenta actualmente la vía de estudio. Asimismo, hace énfasis en la necesidad de contar con una estrategia que nos permita intervenir oportunamente y brindar un mantenimiento, rehabilitación o sustitución de la capa de rodadura si es necesario. Este trabajo de investigación empieza con la división de la vía en secciones o “unidades de muestreo” de 35.4 m cada una, dando un total de 54 unidades de muestra en el carril izquierdo y 41 en el carril derecho, de los cuales fueron estudiadas 13 y 12 respectivamente, haciendo un total de 25 unidades de muestra sometidas a evaluación de acuerdo a lo estipulado en el manual PCI. Una vez realizado el seccionamiento de la vía y selección de las muestras a estudiar, se procedió a la inspección visual estudiando las fallas existentes, así como su severidad y cantidad. Toda la información obtenida en el trabajo de campo fue recolectada en formatos normalizados por la metodología. Por último, se realizó el procesamiento de datos según el manual PCI y se obtuvo como resultado un valor de PCI igual a 49.21 para el carril derecho y 51.02 para el carril izquierdo; según los rangos de calificación de la metodología ambos valores corresponden a un estado “REGULAR”.The main objective of this thesis work is to determine the condition index of the flexible pavement in the Metropolitan Avenue II of the city of Trujillo in the use of the methology of the PCI (Pavement Condition Index), it means, it is precise the level of degradation that the study path currently presents. So, it makes emphasis in the need to have a strategy that allows us to intervenue opportunely and provide a maintenance service, rehabilitation or replacement of the asphalt if necessary. This research work begins with the division of the road into sections or ""sampling units"" of 35.4 m each one, giving a total of 54 sample units in the left lane and 41 in the right lane, of which have been studied 13 and 12 respectively, making a total of 25 sample units in an evaluation according to what is stipulated in the PCI manual. Once the sectioning of the road and the selection of the study samples, the visual inspection was proceeded, studying existing faults, as well as their severity and quantity. All the information in the field work is collected in the formats standardized by the methodology. Finally, the data was processed according to the PCI manual and a the result is a value of 49.21 for the right lane and 51.02 for the left lane; according to the rating ranges of the methodology both values corresponding to a ""REGULAR"" status.Tesi

Effects of single injection of naloxone and damgo within nucleus accumbens septi in the plus maze test in rats

Nucleus accumbens septi (NAS) is studied because its relations with cognition and anxiety. Its pharmacological manipulation is widely used in experimental psychopathology to reproduce psychotic signs and symptoms in animal models. In the present study, the effect of the injection of an agonist and a µ-receptor antagonist in this structure is assessed. Holtzman strain male rats (240-290 g) were cannulated bilaterally in NAS. One week after the injection they were subjected to an anxiety test, prior saline injection (controls), DAMGO ([D-Ala2, N-MePhe4, Gly-ol]-encephalin, opioid agonist) or naloxone (opioid antagonist). We evaluated the set of parameters classically considered in our laboratory (open arm time, time per entry, open arm entries, closed arm entries, open/closed arm quotient, open and closed arm ends arrivals, rearing, fecal bowls and grooming behaviors. There was only a significant increase in the length of stay in the open arm with the injection of DAMGO (0.2 µg/1 µL, p < 0.05) and a significant increase in grooming behaviors with naloxone (1 µg/1 µL, p < 0.001), compared with saline controls (1 µL). We conclude that the receptor stimulation in NAS generates effects compatible with anxiolysis, and blocking of such receptor in said structure results in an increase in grooming behaviors.Fil: Morsucci C. Universidad Nacional de Cuyo; ArgentinaFil: Okasova A. Universidad Nacional de Cuyo; ArgentinaFil: Mulet, Daniela. Universidad Nacional de Cuyo; ArgentinaFil: Galiana, Graciana. Universidad Nacional de Cuyo; ArgentinaFil: Rodriguez, Vanesa. Universidad Nacional de Cuyo; ArgentinaFil: Baiardi, Gustavo Carlos. Universidad Católica de Córdoba. Facultad de Ciencias Químicas; ArgentinaFil: Lafuente, José Vicente. Universidad Nacional de Cuyo; ArgentinaFil: Elias, Pablo Adolfo. Universidad Nacional de Cuyo; ArgentinaFil: Landa, Adriana. Universidad Nacional de Cuyo; ArgentinaFil: Soaje, Marta. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; ArgentinaFil: Gargiulo, Pascual Angel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Laser-Based Reactive Navigation for Multirotor Aerial Robots using Deep Reinforcement Learning

Navigation in unknown indoor environments with fast collision avoidance capabilities is an ongoing research topic. Traditional motion planning algorithms rely on precise maps of the environment, where re-adapting a generated path can be highly demanding in terms of computational cost. In this paper, we present a fast reactive navigation algorithm using Deep Reinforcement Learning applied to multi rotor aerial robots. Taking as input the 2D-laser range measurements and the relative position of the aerial robot with respect to the desired goal, the proposed algorithm is successfully trained in a Gazebo-based simulation scenario by adopting an artificial potential field formulation. A thorough evaluation of the trained agent has been carried out both in simulated and real indoor scenarios, showing the appropriate reactive navigation behavior of the agent in the presence of static and dynamic obstacles

Towards fully autonomous landing on moving platforms for rotary Unmanned Aerial Vehicles

Fully autonomous landing on moving platforms poses a problem of importance for Unmanned Aerial Vehicles (UAVs). Current approaches are usually based on tracking and following the moving platform by means of several techniques, which frequently lack performance in real applications. The aim of this paper is to prove a simple landing strategy is able to provide practical results. The presented approach is based on three stages: estimation, prediction and fast landing. As a preliminary phase, the problem is solved for a particular case of the IMAV 2016 competition. Subsequently, it is extended to a more generic and versatile approach. A thorough evaluation has been conducted with simulated and real flight experiments. Simulations have been performed utilizing Gazebo 6 and PX4 Software-In-The-Loop (SITL) and real flight experiments have been conducted with a custom quadrotor and a moving platform in an indoor environment

A Fully-Autonomous Aerial Robot for Search and Rescue Applications in Indoor Environments using Learning-Based Techniques

Search and Rescue (SAR) missions represent an important challenge in the robotics research field as they usually involve exceedingly variable-nature scenarios which require a high-level of autonomy and versatile decision-making capabilities. This challenge becomes even more relevant in the case of aerial robotic platforms owing to their limited payload and computational capabilities. In this paper, we present a fully-autonomous aerial robotic solution, for executing complex SAR missions in unstructured indoor environments. The proposed system is based on the combination of a complete hardware configuration and a flexible system architecture which allows the execution of high-level missions in a fully unsupervised manner (i.e. without human intervention). In order to obtain flexible and versatile behaviors from the proposed aerial robot, several learning-based capabilities have been integrated for target recognition and interaction. The target recognition capability includes a supervised learning classifier based on a computationally-efficient Convolutional Neural Network (CNN) model trained for target/background classification, while the capability to interact with the target for rescue operations introduces a novel Image-Based Visual Servoing (IBVS) algorithm which integrates a recent deep reinforcement learning method named Deep Deterministic Policy Gradients (DDPG). In order to train the aerial robot for performing IBVS tasks, a reinforcement learning framework has been developed, which integrates a deep reinforcement learning agent (e.g. DDPG) with a Gazebo-based simulator for aerial robotics. The proposed system has been validated in a wide range of simulation flights, using Gazebo and PX4 Software-In-The-Loop, and real flights in cluttered indoor environments, demonstrating the versatility of the proposed system in complex SAR missions

Stereo Visual Odometry and Semantics based Localization of Aerial Robots in Indoor Environments

In this paper we propose a particle filter localization approach, based on stereo visual odometry (VO) and semantic information from indoor environments, for mini-aerial robots. The prediction stage of the particle filter is performed using the 3D pose of the aerial robot estimated by the stereo VO algorithm. This predicted 3D pose is updated using inertial as well as semantic measurements. The algorithm processes semantic measurements in two phases; firstly, a pre-trained deep learning (DL) based object detector is used for real time object detections in the RGB spectrum. Secondly, from the corresponding 3D point clouds of the detected objects, we segment their dominant horizontal plane and estimate their relative position, also augmenting a prior map with new detections. The augmented map is then used in order to obtain a drift free pose estimate of the aerial robot. We validate our approach in several real flight experiments where we compare it against ground truth and a state of the art visual SLAM approach

Accuracy of a Smartwatch to Assess Heart Rate Monitoring and Atrial Fibrillation in Stroke Patients

Accuracy; Atrial fibrillation; SmartwatchPrecisión; Fibrilación auricular; Reloj inteligentePrecisió; Fibril·lació auricular; Rellotge intel·ligentBackground: Consumer smartwatches may be a helpful tool to screen for atrial fibrillation (AF). However, validation studies on older stroke patients remain scarce. The aim of this pilot study from RCT NCT05565781 was to validate the resting heart rate (HR) measurement and the irregular rhythm notification (IRN) feature in stroke patients in sinus rhythm (SR) and AF. (2) Methods: Resting clinical HR measurements (every 5 min) were assessed using continuous bedside ECG monitoring (CEM) and the Fitbit Charge 5 (FC5). IRNs were gathered after at least 4 h of CEM. Lin’s concordance correlation coefficient (CCC), Bland-Altman analysis, and mean absolute percentage error (MAPE) were used for agreement and accuracy assessment. (3) Results: In all, 526 individual pairs of measurements were obtained from 70 stroke patients—age 79.4 years (SD ± 10.2), 63% females, BMI 26.3 (IQ 22.2–30.5), and NIHSS score 8 (IQR 1.5–20). The agreement between the FC5 and CEM was good (CCC 0.791) when evaluating paired HR measurements in SR. Meanwhile, the FC5 provided weak agreement (CCC 0.211) and low accuracy (MAPE 16.48%) when compared to CEM recordings in AF. Regarding the accuracy of the IRN feature, analysis found a low sensitivity (34%) and high specificity (100%) for detecting AF. (4) Conclusion: The FC5 was accurate at assessing the HR during SR, but the accuracy during AF was poor. In contrast, the IRN feature was acceptable for guiding decisions regarding AF screening in stroke patients.This study was funded by the Instituto de Salud Carlos III and the European Union (ERDF/ESF)—A way to build Europe (PI20/01210). Funding was also received in the framework of the “Digital Health Research Promotion Program: from the idea to the project” from the eHealth Center of the Universitat Oberta de Catalunya (UOC)

Fast and Robust Flight Altitude Estimation of Multirotor UAVs in Dynamic Unstructured Environments Using 3D Point Cloud Sensors

This paper presents a fast and robust approach for estimating the flight altitude of multirotor Unmanned Aerial Vehicles (UAVs) using 3D point cloud sensors in cluttered, unstructured, and dynamic indoor environments. The objective is to present a flight altitude estimation algorithm, replacing the conventional sensors such as laser altimeters, barometers, or accelerometers, which have several limitations when used individually. Our proposed algorithm includes two stages: in the first stage, a fast clustering of the measured 3D point cloud data is performed, along with the segmentation of the clustered data into horizontal planes. In the second stage, these segmented horizontal planes are mapped based on the vertical distance with respect to the point cloud sensor frame of reference, in order to provide a robust flight altitude estimation even in presence of several static as well as dynamic ground obstacles. We validate our approach using the IROS 2011 Kinect dataset available in the literature, estimating the altitude of the RGB-D camera using the provided 3D point clouds. We further validate our approach using a point cloud sensor on board a UAV, by means of several autonomous real flights, closing its altitude control loop using the flight altitude estimated by our proposed method, in presence of several different static as well as dynamic ground obstacles. In addition, the implementation of our approach has been integrated in our open-source software framework for aerial robotics called Aerostack