Koordinacija više robota za učinkovite pretraživanje prostora

This paper addresses the problem of exploration of an unknown environment by developing effective exploration strategies for a team of mobile robots equipped with continuously rotating 3D scanners. The main aim of the new strategies is to reduce the exploration time of unknown environment. Unlike most of other published works, to save time, the laser scanners rotate and scan the environment while robots are in motion. Furthermore, the new strategies are able to explore large outdoor environments as a considerable reduction of the required computations, especially those required for path planning, have been achieved. Moreover, another new exploration strategy has been developed so that robots continuously replan the order to visit the remaining unexplored areas according to the new data (i.e. updated map) collected by the robot in question or by the other team members. This new extension led to further enhancements over the above mentioned ones, but with slightly higher computational costs. Finally, to assess our new exploration strategies with different levels of environment complexity, new set of experiments were conducted in environments where obstacles are distributed according to the Hilbert curve. The results of these experiments show the effectiveness of the proposed technique to effectively distribute the robots over the environment. More importantly, we show how the optimal number of robots is related to the environment complexity.Ovaj članak istražuje problem pretraživanja nepoznatog prostora razvijanjem učinkovite strategije za tim mobilnih robota s rotirajućim 3D laserskim senzorom. Glavni cilj ove nove strategije je smanjenje vremena pretraživanja nepoznatog prostora. Za razliku od većine objavljenih radova, u ovome članku, radi smanjenja vremena, laserski senzori rotiraju i snimaju prostor dok su roboti još u pokretu. Predložene strategije, pošto se njima znatno smanjuje računska složenost, pogotovo za planiranje gibanja, omogućuju pretraživanje i vanjskih prostora prostora velikih dimenzija. Nadalje, razvijena je još jedna strategija pretraživanja koja omogućuje robotima da kontinuirano replaniraju poredak kojim će posjetiti ostatak neistraženog prostora, prema novim podacima (ažuriranoj karti) prikupljenim od njih samih ili drugih članova tima. Ovo novo proširenje nadalje unaprjeđuje performanse algoritma, ali uz nešto veću računsku složenost. Kako bi se u konačnici testirale nove strategije pretraživanja na prostorima različite složenosti, provedeni su eksperimenti s preprekama raspoređenim po Hilbertovoj krivulji. Rezultati eksperimenata pokazuju učinkovitost predložene metode u prostornom raspoređivanju robota. Od posebne je važnosti istaknuti da se u članku također istražuje odnos između broja robota i kompleksnosti prostora

Semantische dreidimensionale Karten für autonome mobile Roboter

Intelligentes autonomes Roboterhandeln in Alltagsumgebungen erfordert den Einsatz von 3D-Karten, in denen Objekte klassifiziert sind. 3D-Karten sind u.a. zur Steuerung notwendig, damit der Roboter komplexen Hindernissen ausweichen und sich mit 6 Freiheitsgraden (x-, y-, z-Position, Nick-, Gier-, und Rollwinkel) lokalisieren kann. Soll der Roboter mit seiner Umgebung interagieren, wird Interpretation unumgänglich. Über erkannte Objekte kann der Roboter Schlussfolgerungen ziehen, sein Wissen wird inspizier- und kommunizierbar. Aus diesen Gründen ist die automatische und schnelle semantische 3D-Modellierung der Umgebung eine wichtige Fragestellung in der Robotik. 3D-Laserscanner sind eine junge Technologie, die die Erfassung räumlicher Daten revolutioniert und Robotern das dreidimensionale Abtasten von Objekten möglich macht. Die vorliegende Arbeit untersucht und evaluiert mit Hilfe eines 3D-Laserscanners und des mobilen Roboters Kurt3D die zur automatischen semantischen 3D-Kartenerstellung notwendigen Algorithmen. Der erste Teil der Arbeit beschäftigt sich mit der Aufgabe, 3D-Scans in einem globalen Koordinatensystem zu registrieren. Korrekte, global konsistente Modelle entstehen durch einen 6D-SLAM Algorithmus. Hierbei werden 6 Freiheitsgrade in der Roboterpose berücksichtigt, geschlossene Kreise erkannt und der globale Fehler minimiert. Die Basis des 6D-SLAM ist ein sehr schneller ICP-Algorithmus. Im zweiten Teil geht es darum, die Punktmodelle mit Semantik zu versehen. Dazu werden 3D-Flächen in einer digitalisierten 3D-Szene detektiert und interpretiert. Anschließend sucht ein effizienter Algorithmus nach Objekten und bestimmt deren Pose, ebenfalls mit 6 Freiheitsgraden. Schließlich wird der in den zahlreichen Experimenten verwendete, mobile Roboter Kurt3D vorgestellt.Semantic three dimensional maps for autonomous mobile robots Intelligent autonomous acting in unstructured environments requires 3D maps with labelled 3D objects. 3D maps are necessary to avoid collisions with complex obstacles and to self localize in six degrees of freedom (x-, y-, z-position, roll, yaw and pitch angle). Meaning becomes inevitable, if the robot has to interact with its environment. The robot is then able to reason about the objects; its knowledge becomes inspectable and communicable. These arguments lead to requiring automatic and fast semantic environment modelling in robotics. A revolutionary method for gaging environments are 3D scanners, which enable robots to scan objects in a non-contact way in three dimensions. The presented work examines and evaluates the algorithms needed for automatic semantic 3D map building using a 3D laser range finder and the mobile robot Kurt3D. The first part deals with the task to register 3D scans in a common coordinate system. Correct, globally consistent models result from a 6D SLAM algorithm. Hereby 6 degrees of freedom of the robot pose are considered, closed-loops are detected and the global error is minimized. 6D SLAM is based on a very fast ICP algorithm. In the second part semantic descriptions are derived from the point model. For that purpose 3D planes are detected and interpreted in the digitalized 3D scene. After that an efficient algorithm detects objects and estimates their pose with 6 degrees of freedom, too. Finally, the mobile robot Kurt3D, that was used in numerous experiments is presented

The RoboCup Rescue Team Deutschland1

The RoboCup Rescue competition aims at boosting research in robots and infrastructure able to help in real rescue missions. The task is to find and report victims in areas of different grades of roughness, which are currently indoor. It challenges to some extreme the mobility of robot platforms as well as the autonomy of their control and sensor interpretation software. In the 2004 competition, the Kurt3D robot was introduced, the first participant capable of mapping its environment in 3D and self-localizing in all six degrees of freedom, i.e., x, y, z positions and roll, yaw and pitch angles. In 2005, we have upgraded the system with more sensors, with a focus on speeding up the algorithms, and we have started to develop a tracked robot platform to cooperate with Kurt3D. This paper gives an introduction to the competition in general and presents main contributions of our Deutschland1 RoboCup Rescue team.