Literatura académica sobre el tema "PX4 autopilote de drone"

Memory corruption error is one of the critical security attack vectors against a wide range of software. Addressing this problem, modern compilers provide multiple features to fortify the software against such errors. However, applying compiler-based memory defense is problematic in legacy systems we often encounter in industry or military environments because source codes are unavailable. In this study, we propose memory diversification techniques tailored for legacy binaries to which we cannot apply state-of- the-art compiler-based solutions. The basic idea of our approach is to automatically patch the machine code instructions of each legacy system differently (e.g., a drone, or a vehicle firmware) without altering any semantic behavior of the software logic. As a result of our system, attackers must create a specific attack payload for each target by analyzing the particular firmware, thus significantly increasing exploit development time and cost. Our approach is evaluated by applying it to a stack and heap of multiple binaries, including PX4 drone firmware and other Linux utilities.

Nowadays, multirotors are versatile systems that can be employed in several scenarios, where their increasing autonomy allows them to achieve complex missions without human intervention. This paper presents a framework for autonomous missions with low-cost Unmanned Aerial Vehicles (UAVs) in Global Navigation Satellite System-denied (GNSS-denied) environments. This paper presents hardware choices and software modules for localization, perception, global planning, local re-planning for obstacle avoidance, and a state machine to dictate the overall mission sequence. The entire software stack has been designed exploiting the Robot Operating System (ROS) middleware and has been extensively validated in both simulation and real environment tests. The proposed solution can run both in simulation and in real-world scenarios without modification thanks to a small sim-to-real gap with PX4 software-in-the-loop functionality. The overall system has competed successfully in the Leonardo Drone Contest, an annual competition between Italian Universities with a focus on low-level, resilient, and fully autonomous tasks for vision-based UAVs, proving the robustness of the entire system design.

Unmanned Aerial Systems (UAS) are becoming more attractive in diverse applications due to their efficiency in performing tasks with a reduced time execution, covering a larger area, and lowering human risks at harmful tasks. In the context of Oil & Gas (O&G), the scenario is even more attractive for the application of UAS for inspection activities due to the large extension of these facilities and the operational risks involved in the processes. Many authors proposed solutions to detect gas leaks regarding the onshore unburied pipeline structures. However, only a few addressed the navigation and tracking problem for the autonomous navigation of UAS over these structures. Most proposed solutions rely on traditional computer vision strategies for tracking. As a drawback, depending on lighting conditions, the obtained path line may be inaccurate, making a strategy to force the UAS to continue on the path necessary. Therefore, this research describes the potential of an autonomous UAS based on image processing technique and Convolutional Neural Network (CNN) strategy to navigate appropriately in complex unburied pipeline networks contributing to the monitoring procedure of the Oil & Gas Industry structures. A CNN is used to detect the pipe, while image processing techniques such as Canny edge detection and Hough Transform are used to detect the pipe line reference, which is used by a line following algorithm to guide the UAS along the pipe. The framework is assessed by a PX4 flight controller Software-in-The-Loop (SITL) simulations performed with the Robot Operating System (ROS) along with the Gazebo platform to simulate the proposed operational environment and verify the approach’s functionality as a proof of concept. Real tests were also conducted. The results showed that the solution is robust and feasible to deploy in this proposed task, achieving 72% of mean average precision on detecting different types of pipes and 0.0111 m of mean squared error on the path following with a drone 2 m away from a tube.

AbstractThis paper introduces a kinodynamic motion planning algorithm for Unmanned Aircraft Systems (UAS), called MP-RRT#. MP-RRT# joins the potentialities of RRT# with a strategy based on Model Predictive Control to efficiently solve motion planning problems under differential constraints. Similar to other RRT-based algorithms, MP-RRT# explores the map constructing an asymptotically optimal graph. In each iteration the graph is extended with a new vertex in the reference state of the UAS. Then, a forward simulation is performed using a Model Predictive Control strategy to evaluate the motion between two adjacent vertices, and a trajectory in the state space is computed. As a result, the MP-RRT# algorithm eventually generates a feasible trajectory for the UAS satisfying dynamic constraints. Simulation results obtained with a simulated drone controlled with the PX4 autopilot corroborate the validity of the MP-RRT# approach.

An uncontrolled fire poses severe threats to both humans and the environment, making firefighting a perilous and complex task. Traditional fire suppression methods are inefficient, costly, and without thorough testing, leading to delays in gaining control over fire outbreaks. This paper presents a novel firefighting drone aimed at mitigating risks to firefighters by extinguishing fires and providing real-time imaging, gas concentration and fire location data monitoring. The proposed intelligent quadcopter utilizes the Pixhawk PX4 microcontroller for precise control and the Pixhawk Telemetry system for data processing. The proposed device is constructed from an ultra-strength S500 Quadcopter frame, NodeMCU, Arduino Nano, various gas sensors, a servo motor to extinguish the fire and a camera to detect fire events in real time. Equipped with an FPV camera and a video transmitter, it transmits live video feed to the ground, enabling efficient navigation using the Flysky I6X controller. The intended position and height of the drone are controlled using an adaptive optimization technique known as fuzzy-based backstepping control. This article demonstrates the effectiveness of the device by collecting and analyzing gas emissions data from controlled burns of various materials. The drone successfully measured concentrations of CO, CO2, O3, SO2, and NO2 in affected areas, providing valuable insights for firefighting operations. Different levels of gases have been measured depending on the concentration from burning alcohol, clothes, plastic materials, paper, leaves, and so on. The novelty of this work lies in the development and comprehensive analysis of an IoT-based firefighting drone conducting extensive real-time experiments.

L'intégration de nouvelles fonctionnalités augmente la complexité des systèmes temps réel , alors que chaque fonctionnalité peut imposer des contraintes de précédences entre les tâches. De plus, la prévalence des multicœurs peut créer l'illusion d'une capacité de calcul élevée. Cependant, cette capacité n'est pas exploitable dans les systèmes critiques en raison de la variabilité des temps d'exécution causé par les nouvelles architecture matérielle qui améliorent le comportement moyen mais pas le pire cas. Cette difficulté s'ajoute à l'existence d'anomalies d'ordonnancement pour les systèmes multicœurs. Dans cette thèse, nous considérons l'ordonnancement partitionné des graphes de précédence. La variabilité des temps d'exécution est décrite par des distributions de probabilité. Nous proposons une Analyse des Temps de Réponse (ATR) basée sur des équations itératives et des opérateurs probabilistes pour des distributions indépendantes. Pour les distributions dépendantes, nous les modélisons par des réseau bayésien. Nous utilisons également la représentation de C-espace et la classification SVM pour estimer la probabilité d'ordonnancabilité. De plus, nous fournissons des techniques de partitionnement et définition des priorités de manière à augmenter le parallélisme. Nous réduisons aussi la complexité de l'analyse en réduisant la taille du graphe sans modifier sa structure
The continuous integration of new functionality increases the complexity of embedded systems, while each functionality might impose precedence constraints between the programs fulfilling it. In addition, the prevalence of several processors may create the illusion of higher computation capacity easing the associated scheduling problem. However, this capacity is not exploitable in critical real time systems because of the increased variability of the execution times due to processor features designed to provide excellent average time behavior and not necessarily ensuring small worst case bounds. This difficulty is added to the existence of scheduling anomalies when the systems are built a top of multi-core processors. In this thesis, we consider partitioned scheduling of DAG tasks defining precedence constraints. The variability of execution times is described by probability distributions. We propose a Response Time Analysis (RTA) based on iterative equations and probabilistic operators for independent distributions. For dependent distributions, we model them using Bayesian network. We also use C-space representation combined with SVM classification to estimate the schedulability probability. Moreover, we provide techniques to define priority and sub-task partitioning in a way to increase parallelism. We also decrease analysis complexity by reducing size of graph without altering the precedence structures

The aim of this thesis is to modify commercially produced drone DJI Matrice 100 and replace its original control unit by open source Pixhawk and its accessories. Subsequently, it deals with the selection of suitable open source firmware for Pixhawk and its configuration on the device. Another part is dedicated to the possibilities of using the Robotic Operating System (ROS) and its Mavros libraries on the onboard computer Raspberry Pi. By using Mavros, it examines the possibilities of drone flight control, both in the simulation environment and in the real environment.

Les systèmes embarqués en temps réel, malgré leurs ressources limitées, évoluent très rapidement. Pour ces systèmes, il est impératif de garantir que les tâches ne manquent pas leurs échéances, mais aussi la bonne qualité des données transmises de tâche en tâche. Il est obligatoire de trouver des compromis entre les contraintes d'ordonnancement du système et celles appliquées aux données. Pour garantir ces propriétés, nous considérons le mécanisme sans attente. L'accès aux ressources partagées suit le principe d'un seul producteur, plusieurs lecteurs. Pour contenir toutes les particularités de communication apportées par le mécanisme de communication uORB, nous avons modélisé les interactions entre les tâches par un graphe biparti que nous avons appelé graphe de communication et qui est composé d'ensembles de messages dits de domaine. Pour améliorer la prévisibilité de la communication inter-tâches, nous étendons le modèle de Liu & Layland avec le paramètre état de communication utilisé pour contrôler les points d'écriture/lecture.Nous avons considéré deux types de contraintes de données : les contraintes locales de données et les contraintes globales de données. Pour vérifier les contraintes locales des données, nous nous appuyons sur le mécanisme de sous-échantillonnage destiné à vérifier les contraintes locales des données. En ce qui concerne les contraintes globales des données, nous avons introduit deux nouveaux mécanismes : le " dernier lecteur de marque" et le " mécanisme de défilement ou d'écrasement ". Ces 2 mécanismes sont en quelque sorte complémentaires. Le premier fonctionne au début du fuseau tandis que le second fonctionne à la fin du fuseau
Real-time embedded systems, despite their limited resources, are evolving very quickly. For such systems, it is not enough to ensure that all jobs do not miss their deadlines, it is also mandatory to ensure the good quality of the data being transmitted from tasks to tasks. Speaking of the data quality constraints, they are expressed by the maintenance of a set of properties that a data sample must exhibit to be considered as relevant. It is mandatory to find trade-offs between the system scheduling constraints and those applied to the data. To ensure such properties, we consider the wait-free mechanism. The size of each communication buffer is based on the lifetime bound method. Access to the shared resources follows the single writer, many readers. To contain all the communication particularities brought by the uORB communication mechanism we modeled the interactions between the tasks by a bipartite graph that we called communication graph which is comprised of sets of so-called domain messages. To enhance the predictability of inter-task communication, we extend Liu and Layland model with the parameter communication state used to control writing/reading points.We considered two types of data constraints: data local constraints and data global constraints. To verify the data local constraints, we rely on the sub-sampling mechanism meant to verify data local constraints. Regarding the data global constraints, we introduced two new mechanism: the last reader tags mechanism and the scroll or overwrite mechanism. These 2 mechanisms are to some extent complementary. The first one works at the beginning of the spindle while the second one works at the end of the spindle