An expanded square pattern technique in swarm of quadcopters for exploration algorithm

The exploration algorithm is one of the most important roles in the searching mechanism. In robotics field, the exploration algorithm deals with the implementation of the robot to enlarge the information over a particular environment. In other words, the implementation of exploration algorithm into a robot is intended to survey the situation or condition of a specific area. A variety of techniques has been developed, even the biological systems also become an inspiration to be reckoned. In this paper, we proposed a swarmbased exploration algorithm with expanded square pattern using a quadcopter to explore an unknown area. In this algorithm, the expanded square pattern is conducted by a series of distance around a fixed reference point. We simulate the swarm-based exploration algorithm with expanded square pattern using a VREP simulator. The existing exploration algorithms that have been identified are also simulated to be compared with the proposed algorithm. In order to analysed and evaluate the performance of all algorithms, the data of simulation is documented. Some comparisons are conducted such as the performance of all algorithms, the performance of a group of the quadcopter, the covered spaces and the cooperation among groups

GATSBI: An Online GTSP-Based Algorithm for Targeted Surface Bridge Inspection

We study the problem of visually inspecting the surface of a bridge using an Unmanned Aerial Vehicle (UAV) for defects. We do not assume that the geometric model of the bridge is known. The UAV is equipped with a LiDAR and RGB sensor that is used to build a 3D semantic map of the environment. Our planner, termed GATSBI, plans in an online fashion a path that is targeted towards inspecting all points on the surface of the bridge. The input to GATSBI consists of a 3D occupancy grid map of the part of the environment seen by the UAV so far. We use semantic segmentation to segment the voxels into those that are part of the bridge and the surroundings. Inspecting a bridge voxel requires the UAV to take images from a desired viewing angle and distance. We then create a Generalized Traveling Salesperson Problem (GTSP) instance to cluster candidate viewpoints for inspecting the bridge voxels and use an off-the-shelf GTSP solver to find the optimal path for the given instance. As more parts of the environment are seen, we replan the path. We evaluate the performance of our algorithm through high-fidelity simulations conducted in Gazebo. We compare the performance of this algorithm with a frontier exploration algorithm. Our evaluation reveals that targeting the inspection to only the segmented bridge voxels and planning carefully using a GTSP solver leads to more efficient inspection than the baseline algorithms.Comment: 8 pages, 16 figure

Combining Stochastic Optimization and Frontiers for Aerial Multi-Robot Exploration of 3D Terrains

International audienceThis paper addresses the problem of exploring unknown terrains with a fleet of cooperating aerial vehicles. We present a novel decentralized approach which alternates gradient-free stochastic optimization and frontier-based approaches. Our method allows each robot to generate its trajectory based on the collected data and the local map built integrating the information shared by its team-mates. Whenever a local optimum is reached, which corresponds to a location surrounded by already explored areas, the algorithm identifies the closest frontier to get over it and restarts the local optimization. Its low computational cost, the capability to deal with constraints and the decentralized decision-making make it particularly suitable for multi-robot applications in complex 3D environments. Simulation results show that our approach generates feasible and safe trajectories which drive multiple robots to completely explore realistic environments. Furthermore, in terms of exploration time, our algorithm significantly outperforms a standard solution based on closest frontier points while providing similar performances compared to a computationally more expensive centralized greedy solution

Exploración de fronteras para robots móviles en interiores

Desarrollar un algoritmo de exploración de fronteras para un robot móvil en interiores.En la actualidad el uso de robots móviles está aumentando exponencialmente en todo el mundo y son utilizados en algunas aplicaciones como explorar áreas que son inalcanzables o peligrosas para los seres humanos, motivo por el cual se busca un robot móvil que sea capaz de realizar esta tarea de forma autónoma. Para explorar un área y obtener un mapa se usa algo- ritmos de exploración que pueden tener diferentes enfoques para seleccionar sus objetivos de navegación, según los parámetros establecidos para su alcance y desplazamiento programados. La finalidad de este proyecto es desarrollar un algoritmo de exploración basado en fronteras para áreas en interiores y probarlo en un robot móvil Turtlebot 2. Para lograr esto como primer punto se realiza un estudio del estado del arte basado en algoritmos de exploración de fronteras para analizar las diferentes metodologías que se emplean. Y poder optar por un criterio en el desarrollo del algoritmo. Para demostrar su funcionamiento es necesario usar la técnica de navegación Localización y Mapeo Simultáneo (SLAM, Simultáneos Localization and Mapping) y obtener la odometr´ia del robot, lo cual sirve para la creación de un mapa. Todo esto se desarrolla en el Sistema Operativo de Robot (ROS, Robot Operating System). Una vez desarrollado el algoritmo se procede a ejecutar pruebas de funcionamiento con dos variantes de un algoritmo de exploración de fronteras. Para ello se ha creado cinco entornos diferentes donde se puede analizar su funcionamiento tomando en cuenta dos parámetros.Ingenierí

Active Mapping and Robot Exploration: A Survey

Simultaneous localization and mapping responds to the problem of building a map of the environment without any prior information and based on the data obtained from one or more sensors. In most situations, the robot is driven by a human operator, but some systems are capable of navigating autonomously while mapping, which is called native simultaneous localization and mapping. This strategy focuses on actively calculating the trajectories to explore the environment while building a map with a minimum error. In this paper, a comprehensive review of the research work developed in this field is provided, targeting the most relevant contributions in indoor mobile robotics.This research was funded by the ELKARTEK project ELKARBOT KK-2020/00092 of the Basque Government

A Common Optimization Framework for Multi-Robot Exploration and Coverage in 3D Environments

International audienceThis paper studies the problems of static coverage and autonomous exploration of unknown three-dimensional environments with a team of cooperating aerial vehicles. Although these tasks are usually considered separately in the literature, we propose a common framework where both problems are formulated as the maximization of online acquired information via the definition of single-robot optimization functions, which differs only slightly in the two cases to take into account the static and dynamic nature of coverage and exploration respectively. A common derivative-free approach based on a stochastic approximation of these functions and their successive optimization is proposed, resulting in a fast and decentralized solution. The locality of this methodology limits however this solution to have local optimality guarantees and specific additional layers are proposed for the two problems to improve the final performance. Specifically, a Voronoi-based initialization step is added for the coverage problem and a combination with a frontier-based approach is proposed for the exploration case. The resulting algorithms are finally tested in simulations and compared with possible alternatives

Experimentelle Evaluation und Vergleich von technischen Systemen der 3D-Erfassung und -Rekonstruktion auf die Geeignetheit der gefahrlosen Erkundung und Lagedarstellung von Gebäudeinnenräumen für die Gefahrenabwehr

Im Rahmen dieser Bachelorarbeit an der Technischen Hochschule Köln, deren Bearbeitungszeit vom 20.1.2017 bis 24.3.2017 ist, wird durch ein technisches Experiment geprüft, ob diverse Technologien zur 3D-Erfassung und Rekonstruktion geeignet sind Gebäudeinnenräume so zu erkunden, dass die Gefahrenabwehr dadurch einen höheren Nutzen hat, als es bei einer persönlichen oder autonomen Videoerkundung der Fall ist. Dies soll vor allem dann helfen, wenn Gebäude nicht mehr betreten werden sollten, wie es beispielsweise der Fall ist, wenn das Gebäude durch Erdbebenschäden einsturzgefährdet oder mit Gasen kontaminiert ist. 3D-Modelle einer Umgebung haben u.a. den Vorteil, dass problemlos neue Blickwinkel eingenommen, Maße ermittelt und Planungen für Rettungseinsätze oder Evakuierungen effizienter durchgeführt werden können, ohne Bildmaterial aufwendig zu sichten. Zudem können die Ergebnisse für spätere Evaluationen und Trainings genutzt werden. Um diese Geeignetheit festzustellen werden Beurteilungskriterien erarbeitet, die ein potentielles System erfüllen muss. Diese Kriterien sind: Günstig in der Beschaffung, Zeit bis zu einer 3D-Darstellung, leichte Bedienbarkeit, Qualität bzw. Informationsgewinnung aus der Darstellung (Erkennung von Zugängen und Personen), Lieferung von Zusatzinformationen (beispielsweise Maßangaben) und ob das System Online oder Offline funktionsfähig ist. Um diese Kriterien beurteilen zu können, werden Systeme der drei Haupttechnologien in der 3D-Erfassung (Laserscanner GeoSLAM ZEB-REVO, RGB-D-Kamera Microsoft Kinect und FARO Freestyle3D, Fotogrammetrie mit der Software Agisoft PhotoScan) in einem Versuch überprüft. Dabei wird das Labor für Großschadensereignisse der Technischen Hochschule Köln, der angrenzende Flur und das angrenzende Treppenhaus gescannt bzw. erfasst und rekonstruiert, wobei die nötigen Daten ermittelt werden. Dabei stellt sich heraus, dass der FARO Freestyle3D mit seiner RGB-D-Technologie und der Software FARO Scene als einziges System alle Kriterien erfüllt und somit für den Zweck der Erkundung in diesem Kontext geeignet ist. Der Microsoft Kinect Sensor mit der Software FARO Scene hat, durch Fehler in der Rekonstruktion, Schwächen in der Darstellungsqualität. Dies gilt auch für die Kombination aus GeoS-LAM ZEB-REVO/CloudCompare, da hiermit kein Farbscan erstellt werden kann und somit eine Erkennung von Objekten (z.B. geschlossene Türen) erschwert wird. Die Fotogrammetrie dauert mit einer Berechnungsdauer von ca. 38 Stunden zu lange, um in Notfallsituationen einen Nutzen zu bieten und liefert außerdem falsche Maßangaben. Nicht näher betrachtet wird in dieser Arbeit die (autonome) Erkundung durch Roboter oder Drohnen, die die Geräte transportieren können, Strom- und Datenübertragungsproblematiken, andere ergänzende Sensortechniken und die Erkundung in dunkler Umgebung

Autonomous Robot Exploration with Selective Object Discrimination by Using Deep Learning Object Detection

Since the geopolitical world is not polarized anymore, the market competitivity is increasing as never before so in order to survive as an industrial organization, it is key to be competitive. That is, reducing costs and production times among other needs. Mobile robots are resources that manage to get those needs relieved since they can substitute humans and perform better. This causes human issues casuistic drop, human resources re-allocation in more creative job positions which cannot replaced by robots, and more long-term efficiency. The state-of-the-art of the use of mobile robots remains on the fact that we are talking about not just a single mobile robot but a fleet of them which performs in a smart and coordinated way. These devices can be integrated in the supply-chain so that can transport payloads without the need of any human intervention. In addition, such integration allows a huge flexibility since smart industrial mobile robots can adapt to new conditions, imposed parameters and obstacles that were not predicted. For any autonomous mobile robot, a prior knowledge about its environment is necessary before performing autonomous navigation, that is to have a previous map. Mapping usually is a human intervened task which takes time, especially for large facilities. This work proposes a way to map autonomously, in the most efficient way, an indoor 2D environment by using the Rapidly-exploring Random Trees approach since it is biased towards unknown regions. In addition, this work proposes object discrimination during mapping. With the conventional approach, during the mapping process laser scanners read the presence of all the obstacles in the environment. This fact is undesired since some of such scanned obstacles are scanned just by causality during the exploration (e.g. personnel, industrial mobile equipment…). Such undesired registered data in the map suppose noise and does not represent the actual long-term environment. The implementation of removing such noise is managed by the combination of two modules. On one hand, by using state-of-the-art deep learning tools in order to achieve real-time object detection. On the other hand, a filter to the laser scanner so that it is blind towards such detections during the exploration, so they are never registered on the map. The results show quite potential high-quality results which are intrinsically associated with the object detector module. Since such module is state-of-the-art, the technology involved is constantly developing and improving not just the performance but also flexibility and capabilities. This work is a potential new high-fidelity approach besides the conventional approach in order to perform mobile robot exploration