X-COPTER STUDIO

We present a project that aggregates various existing robotic software and serves as a platform to conveniently control a quadrocopter, mainly for research or educational purposes. User interface runs in a browser and other components are also made with portability in mind. We provide a common interface that unifies different quadrocopter models and we implemented it for the Parrot AR.Drone 2.0. The platform is data oriented, i.e., it is based on dataflow between user objects. We implemented several such objects for: data recording and replaying, inertial and visual localization and following a given path

Navigation, localization and stabilization of formations of unmanned aerial and ground vehicles

A leader-follower formation driving algorithm developed for control of heterogeneous groups of unmanned micro aerial and ground vehicles stabilized under a top-view relative localization is presented in this paper. The core of the proposed method lies in a novel avoidance function, in which the entire 3D formation is represented by a convex hull projected along a desired path to be followed by the group. Such a representation of the formation provides non-collision trajectories of the robots and respects requirements of the direct visibility between the team members in environment with static as well as dynamic obstacles, which is crucial for the top-view localization. The algorithm is suited for utilization of a simple yet stable visual based navigation of the group (referred to as GeNav), which together with the on-board relative localization enables deployment of large teams of micro-scale robots in environments without any available global localization system. We formulate a novel Model Predictive Control (MPC) based concept that enables to respond to the changing environment and that provides a robust solution with team members' failure tolerance included. The performance of the proposed method is verified by numerical and hardware experiments inspired by reconnaissance and surveillance missions

Software for Embedded Module for Image Processing

katedra kybernetik

Fault-tolerant formation driving mechanism designed for heterogeneous MAVs-UGVs groups

A fault-tolerant method for stabilization and navigation of 3D heterogeneous formations is proposed in this paper. The presented Model Predictive Control (MPC) based approach enables to deploy compact formations of closely cooperating autonomous aerial and ground robots in surveillance scenarios without the necessity of a precise external localization. Instead, the proposed method relies on a top-view visual relative localization provided by the micro aerial vehicles flying above the ground robots and on a simple yet stable visual based navigation using images from an onboard monocular camera. The MPC based schema together with a fault detection and recovery mechanism provide a robust solution applicable in complex environments with static and dynamic obstacles. The core of the proposed leader-follower based formation driving method consists in a representation of the entire 3D formation as a convex hull projected along a desired path that has to be followed by the group. Such an approach provides non-collision solution and respects requirements of the direct visibility between the team members. The uninterrupted visibility is crucial for the employed top-view localization and therefore for the stabilization of the group. The proposed formation driving method and the fault recovery mechanisms are verified by simulations and hardware experiments presented in the paper

A New Three Object Triangulation Algorithm for Mobile Robot Positioning

Positioning is a fundamental issue in mobile robot applications. It can be achieved in many ways. Among them, triangulation based on angles measured with the help of beacons is a proven technique. Most of the many triangulation algorithms proposed so far have major limitations. For example, some of them need a particular beacon ordering, have blind spots, or only work within the triangle defined by the three beacons. More reliable methods exist; however, they have an increasing complexity or they require to handle certain spatial arrangements separately. In this paper, we present a simple and new three object triangulation algorithm, named ToTal, that natively works in the whole plane, and for any beacon ordering. We also provide a comprehensive comparison between many algorithms, and show that our algorithm is faster and simpler than comparable algorithms. In addition to its inherent efficiency, our algorithm provides a very useful and unique reliability measure, assessable anywhere in the plane, which can be used to identify pathological cases, or as a validation gate in Kalman filters.Peer reviewe

Small Unmanned Aircraft Systems for Project-Based Engineering Education

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143092/1/6.2017-1377.pd

Planificación en sistemas robotizados mediante PDDL y ROS

La planificación automática se encarga de generar un plan con las distintas acciones que logran cumplir los objetivos definidos. Esta habilidad resulta útil en sistemas robotizados, porque permite definir el comportamiento de uno o varios robots sin tener que predefinir las acciones que consiguen cumplir un objetivo. En este trabajo presentaremos las principales herramientas que se pueden utilizar para integrar sistemas de planificación automática en entornos robotizados mediante el uso de ROS. Además, estas herramientas se integrarán en sistemas robóticos reales mediante el uso del simulador Gazebo, lo que permitirá una mayor comprensión sobre el funcionamiento del sistema.Automatic planning is responsible for generating a plan with the di erent actions that achieve the de ned objectives. This ability is useful in robotic systems, because it allows de ning the behavior of one or more robots without having to prede ne the actions that achieve a goal. In this work we will present the main tools that can be used to integrate automatic planning systems in robotic environments through the use of ROS. In addition, these tools will be integrated into real robotic systems using the Gazebo simulator, which will allow a greater understanding of the system.Grado en Ingeniería en Electrónica y Automática Industria

Risk-sensitive motion planning for MAVs based on mission-related fault-tolerant analysis

Multirotor Aerial Vehicles may be fault-tolerant by design when rotor-failure is possible to measure or identify, especially when a large number of rotors are used. For instance, an octocopter can be capable to complete some missions even when a double-rotor fault occurs during the execution. In this paper, we study how a rotor-failure reduces the vehicle control admissible set and its importance with respect to the selected mission, i.e. we perform mission-related fault-tolerant analysis. Furthermore, we propose a risk-sensitive motion-planning algorithm capable to take into account the risks during the planning stage by means of mission-related fault-tolerant analysis. We show that the proposed approach is much less conservative in terms of selected performance measures than a conservative risk planner that assumes that the considered fault will certainly occur during the mission execution. As expected, the proposed risk-sensitive motion planner is also readier for accepting failures during the mission execution than the risk-insensitive approach that assumes no failure will occur