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  • 1.
    Akin, H. Levent
    et al.
    Bogazici University, Turkey.
    Ito, Nobuhiro
    Aichi Institute of Technology, Japan.
    Jacoff, Adam
    National Institute of Standards, USA.
    Kleiner, Alexander
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Pellenz, Johannes
    V&R Vision & Robotics GmbH, Germany.
    Visser, Arnoud
    University of Amsterdam, Holland.
    RoboCup Rescue Robot and Simulation Leagues2013In: The AI Magazine, ISSN 0738-4602, Vol. 34, no 1Article in journal (Refereed)
    Abstract [en]

    The RoboCup Rescue Robot and Simulation competitions have been held since 2000. The experience gained during these competitions has increased the maturity level of the field, which allowed deploying robots after real disasters (e.g. Fukushima Daiichi nuclear disaster). This article provides an overview of these competitions and highlights the state of the art and the lessons learned.

  • 2.
    Balakirsky, Stephen
    et al.
    National Institute of Standards and Technology.
    Carpin, Stefano
    University of California Merced.
    Kleiner, Alexander
    University of Freiburg.
    Lewis, Michael
    University of Pittsburgh.
    Visser, Arnoud
    Universiteit van Amsterdam.
    Wang, Jijun
    University of Pittsburgh.
    Ziparo, Vittorio Amos
    Universita ́ di Roma “Sapienza”.
    Towards Heterogeneous Robot Teams for Disaster Mitigation: Results and Performance Metrics from Robocup Rescue2007In: Journal of Field Robotics, ISSN 1556-4959, Vol. 24, no 11, p. 943-967Article in journal (Refereed)
    Abstract [en]

    Urban Search And Rescue is a growing area of robotic research. The RoboCup Federation has recognized this, and has created the new Virtual Robots competition to complement its existing physical robot and agent competitions. In order to successfully compete in this competition, teams need to field multi-robot solutions that cooperatively explore and map an environment while searching for victims. This paper presents the results of the first annual RoboCup Rescue Virtual competition. It provides details on the metrics used to judge the contestants as well as summaries of the algorithms used by the top four teams. This allows readers to compare and contrast these effective approaches. Furthermore, the simulation engine itself is examined and real-world validation results on the engine and algorithms are offered.

  • 3.
    Bock, Alexander
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Kleiner, Alexander
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Lundberg, Jonas
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Ropinski, Timo
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Supporting Urban Search & Rescue Mission Planning through Visualization-Based Analysis2014In: Proceedings of the Vision, Modeling, and Visualization Conference 2014, Eurographics - European Association for Computer Graphics, 2014Conference paper (Refereed)
    Abstract [en]

    We propose a visualization system for incident commanders in urban search~\&~rescue scenarios that supports access path planning for post-disaster structures. Utilizing point cloud data acquired from unmanned robots, we provide methods for assessment of automatically generated paths. As data uncertainty and a priori unknown information make fully automated systems impractical, we present a set of viable access paths, based on varying risk factors, in a 3D environment combined with the visual analysis tools enabling informed decisions and trade-offs. Based on these decisions, a responder is guided along the path by the incident commander, who can interactively annotate and reevaluate the acquired point cloud to react to the dynamics of the situation. We describe design considerations for our system, technical realizations, and discuss the results of an expert evaluation.

  • 4.
    Burgard, W.
    et al.
    University of Freiburg.
    Stachniss, C.
    University of Freiburg.
    Grisetti, G.
    University of Freiburg.
    Steder, B.
    University of Freiburg.
    Kümmerle, R.
    University of Freiburg.
    Dornhege, C.
    University of Freiburg.
    Ruhnke, M.
    University of Freiburg.
    Kleiner, Alexander
    University of Freiburg.
    Tardós, Juan D.
    University of Freiburg.
    A Comparison of SLAM Algorithms Based on a Graph of Relations2009In: IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), IEEE conference proceedings, 2009, p. 2089-2095Conference paper (Refereed)
    Abstract [en]

    In this paper, we address the problem of creating an objective benchmark for comparing SLAM approaches. We propose a framework for analyzing the results of SLAM approaches based on a metric for measuring the error of the corrected trajectory. The metric uses only relative relations between poses and does not rely on a global reference frame. The idea is related to graph-based SLAM approaches, namely to consider the energy that is needed to deform the trajectory estimated by a SLAM approach into the ground truth trajectory. Our method enables us to compare SLAM approaches that use different estimation techniques or different sensor modalities since all computations are made based on the corrected trajectory of the robot. We provide sets of relative relations needed to compute our metric for an extensive set of datasets frequently used in the SLAM community. The relations have been obtained by manually matching laser-range observations to avoid the errors caused by matching algorithms. Our benchmark framework allows the user an easy analysis and objective comparisons between different SLAM approaches.

  • 5.
    Conte, Gianpaolo
    et al.
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Kleiner, Alexander
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Rudol, Piotr
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Korwel, Karol
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems.
    Wzorek, Mariusz
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Doherty, Patrick
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Performance evaluation of a light weight multi-echo LIDAR for unmanned rotorcraft applications2013In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-1/W2, Copernicus Gesellschaft MBH , 2013Conference paper (Refereed)
    Abstract [en]

    The paper presents a light-weight and low-cost airborne terrain mapping system. The developed Airborne LiDAR Scanner (ALS) sys- tem consists of a high-precision GNSS receiver, an inertial measurement unit and a magnetic compass which are used to complement a LiDAR sensor in order to compute the terrain model. Evaluation of the accuracy of the generated 3D model is presented. Additionally, a comparison is provided between the terrain model generated from the developed ALS system and a model generated using a commer- cial photogrammetric software. Finally, the multi-echo capability of the used LiDAR sensor is evaluated in areas covered with dense vegetation. The ALS system and camera systems were mounted on-board an industrial unmanned helicopter of around 100 kilograms maximum take-off weight. Presented results are based on real flight-test data.

  • 6.
    Dornhege, C.
    et al.
    University of Freiburg.
    Kleiner, Alexander
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems. Linköping University, The Institute of Technology.
    A Frontier-Void-Based Approach for Autonomous Exploration in 3D2013In: Advanced Robotics, ISSN 0169-1864, E-ISSN 1568-5535, Vol. 27, no 6, p. 459-468Article in journal (Refereed)
    Abstract [en]

    We consider the problem of an autonomous robot searching for objects in unknown 3d space. Similar to the well known frontier-based exploration in 2d, the problem is to determine a minimal sequence of sensor viewpoints until the entire search space has been explored. We introduce a novel approach that combines the two concepts of voids, which are unexplored volumes in 3d, and frontiers, which are regions on the boundary between voids and explored space. Our approach has been evaluated on a mobile platform equipped with a manipulator searching for victims in a simulated USAR setup. First results indicate the real-world capability and search efficiency of the proposed method.

  • 7.
    Dornhege, Christian
    et al.
    University of Freiburg.
    Bendler, Johannes
    University of Freiburg.
    Bersan, Roxana
    University of Freiburg.
    Blohm, Philipp
    University of Freiburg.
    Gloderer, Martin
    University of Freiburg.
    Hertle, Andreas
    University of Freiburg.
    Liebetraut, Thomas
    University of Freiburg.
    Puyol, Diego Cerdan
    University of Freiburg.
    Kleiner, Alexander
    University of Freiburg.
    Nebel, Bernhard
    University of Freiburg.
    RoboCupRescue 2010 - Robot League Team RescueRobots Freiburg (Germany)2010In: RoboCup 2010 (CDROM Proceedings), Team Description Paper, Rescue Robot League, 2010Conference paper (Refereed)
    Abstract [en]

    This paper describes the software and hardware system developed by the University of Freiburg team of search and rescue robots for the RoboCup Res- cue 2010 competition. This system is an extension to the software that finished in first place the 2005 and 2006 autonomy challenge, focusing on two key areas: autonomous navigation and manipulation. Our team, consisting mainly of students, originates from the former CS Freiburg team (RoboCupSoccer), the ResQ Freiburg team (RoboCupRescue Simulation), and RescueRobots Freiburg teams ’05 and ’06.

  • 8.
    Dornhege, Christian
    et al.
    University of Freiburg.
    Kleiner, Alexander
    University of Freiburg.
    Behavior Maps for Online Planning of Obstacle Negotiation and Climbing on Rough Terrain2007In: In Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), IEEE conference proceedings, 2007, p. 3005-3011Conference paper (Refereed)
    Abstract [en]

    To autonomously navigate on rough terrain is a challenging problem for mobile robots, requiring the ability to decide whether parts of the environment can be traversed or have to be bypassed, which is commonly known as Obstacle Negotiation (ON). In this paper, we introduce a planning framework that extends ON to the general case, where different types of terrain classes directly map to specific robot skills, such as climbing stairs and ramps. This extension is based on a new concept called behavior maps, which is utilized for the planning and execution of complex skills. Behavior maps are directly generated from elevation maps, i.e. two-dimensional grids storing in each cell the corresponding height of the terrain surface, and a set of skill descriptions. Results from extensive experiments are presented, showing that the method enables the robot to explore successfully rough terrain in real-time, while selecting the optimal trajectory in terms of costs for navigation and skill execution.

  • 9.
    Dornhege, Christian
    et al.
    University of Freiburg.
    Kleiner, Alexander
    University of Freiburg.
    Fully Autonomous Planning and Obstacle Negotiation on Rough Terrain Using Behavior Maps2007In: In Video Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2007, p. 2561-2562Conference paper (Refereed)
    Abstract [en]

    To autonomously navigate on rough terrain is a challenging problem for mobile robots, requiring the ability to decide whether parts of the environment can be traversed or have to be bypassed, which is commonly known as Obstacle Negotiation (ON). In this paper, we introduce a planning framework that extends ON to the general case, where different types of terrain classes directly map to specific robot skills, such as climbing stairs and ramps. This extension is based on a new concept called behavior maps, which is utilized for the planning and execution of complex skills. Behavior maps are directly generated from elevation maps, i.e. two-dimensional grids storing in each cell the corresponding height of the terrain surface, and a set of skill descriptions. Results from extensive experiments are presented, showing that the method enables the robot to explore successfully rough terrain in real-time, while selecting the optimal trajectory in terms of costs for navigation and skill execution.

  • 10.
    Dornhege, Christian
    et al.
    University of Freiburg.
    Kleiner, Alexander
    University of Freiburg.
    Visual Odometry for Tracked Vehicles2006In: In Proc. of the IEEE Int. Workshop on Safety, Security and Rescue Robotics (SSRR), 2006Conference paper (Refereed)
    Abstract [en]

    Localization and mapping on autonomous robots typically requires a good pose estimate, which is hard to acquire if the vehicle is tracked. In this paper we describe a solution to the pose estimation problem by utilizing a consumer-quality camera and an Inertial Measurement Unit (IMU). The basic idea is to continuously track salient features with the KLT feature tracker over multiple images taken by the camera and to extract from the tracked features image vectors resulting from the robot’s motion. Each image vector is taken for a voting that best explains the robot’s motion. Image vectors vote according to a previously trained tile coding classificator that assigns to each possible image vector a translation probability. Our results show that the proposed single camera solution leads to sufficiently accurate pose estimates of the tracked vehicle.

  • 11.
    Dornhege, Christian
    et al.
    University of Freiburg, Germany.
    Kleiner, Alexander
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Kolling, Andreas
    University of Sheffield, UK.
    Coverage Search in 3D2013In: Safety, Security, and Rescue Robotics (SSRR), 2013 IEEE International Symposium on, IEEE , 2013, p. 1-8Conference paper (Refereed)
    Abstract [en]

    Searching with a sensor for objects and to observe parts of a known environment efficiently is a fundamental prob- lem in many real-world robotic applications such as household robots searching for objects, inspection robots searching for leaking pipelines, and rescue robots searching for survivors after a disaster. We consider the problem of identifying and planning efficient view point sequences for covering complex 3d environments. We compare empirically several variants of our algorithm that allow to trade-off schedule computation against execution time. Our results demonstrate that, despite the intractability of the overall problem, computing effective solutions for coverage search in real 3d environments is feasible. 

  • 12.
    Dornhege, Christian
    et al.
    University of Freiburg.
    Kleiner, Alexander
    University of Freiburg.
    Kümmerle, Rainer
    University of Freiburg.
    Steder, Bastian
    University of Freiburg.
    Burgard, Wolfram
    University of Freiburg.
    Nebel, Bernhard
    University of Freiburg.
    SP-Freiburg TechX Challenge Technical Paper2008In: TechX Challenge, 2008Conference paper (Other academic)
    Abstract [en]

    In this paper we introduce our team’s approach to the TechX Challenge, which is based on experiences gathered at RoboCup during the last seven years and recent efforts in robotic research. We particularly focus on Multi-Level Surface (MLS) maps based localization, behavior map based path planning and obstacle negotiation, robot motion planning using a probabilistic roadmap planner, vision and 3D laser supported target detection, which all will be more detailed in the following sections.

  • 13.
    Hamp, Q.
    et al.
    University of Freiburg.
    Reindl, L.
    University of Freiburg.
    Kleiner, Alexander
    University of Freiburg.
    Lessons Learned from German Research for USAR2011In: IEEE Int. Workshop on Safety, Security and Rescue Robotics (SSRR), 2011Conference paper (Other academic)
    Abstract [en]

    We present lessons learned in USAR research within the framework of the German research project I-LOV. After three years of development first field tests have been carried out by professionals such as the Rapid Deployment Unit for Salvage Operations Abroad (SEEBA). We present results from evaluating search teams in simulated USAR scenarios equipped with newly developed technical search means and digital data input terminals developed in the I- LOV project. In particular, the “bioradar”, a ground-penetrating radar system for the detection of humanoid movements, a semi-active video probe for rubble pile exploration of more than 10 m length, and the decision support system FRIEDAA were evaluated and compared with conventional search methods. Results of this evaluation indicate that the developed technologies foster advantages in USAR, which are discussed in this paper.

  • 14.
    Hamp, Quirin
    et al.
    University of Freiburg.
    Gorgis, Omar
    University of Freiburg.
    Labenda, Patrick
    Ruhr-University Bochum.
    Neumann, Marc
    Ruhr-University Bochum.
    Predki, Thomas
    Ruhr-University Bochum.
    Heckes, Leif
    Ruhr-University Bochum.
    Kleiner, Alexander
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Reindl, Leonard
    University of Freiburg.
    Study of efficiency of USAR operations with assistive technologies2013In: Advanced Robotics, ISSN 0169-1864, Vol. 27, no 5, p. 337-350Article in journal (Refereed)
    Abstract [en]

    This paper presents presents a study on eciency of Urban Search and Rescue (USAR) missions that has been carried out within the framework of the German research project I-LOV. After three years of development, first field tests have been carried out in 2011 by professionals such as the Rapid Deployment Unit for Salvage Operations Abroad (SEEBA). We present results from evaluating search teams in simulated USAR scenarios equipped with newly developed technical search means and digital data input terminals developed in the I-LOV project. In particular, USAR missions assisted by the “bioradar”, a ground-penetrating radar system for the detection of humanoid movements, a semi-active video probe of more than 10 m length for rubble pile exploration, a snake-like rescue robot, and the decision support system FRIEDAA were evaluated and compared with conventional USAR missions. Results of this evaluation indicate that the developed technologies represent an advantages for USAR missions, which are discussed in this paper. 

  • 15.
    Kenn, Holger
    et al.
    University of Bremen.
    Kleiner, Alexander
    University of Freiburg.
    Towards the Integration of Real-Time Real-World Data in Urban Search and Rescue Simulation2007In: MobileResponse, 2007, Vol. 4458, p. 106-115Conference paper (Refereed)
    Abstract [en]

    The coordinated reaction to a large-scale disaster is a challenging research problem. The Robocup rescue simulation league addresses this research problem but is currently lacking an interface to real-world real-time data to test the validity of both simulation and simulated reactions. In this paper, we describe a wearable-computing-based real world interface to the Robocup Resuce simulation software and provide some updated results of preliminary evaluations.

  • 16.
    Kleiner, Alexander
    UniversitätFreiburg.
    Game AI: The Shrinking Gap Between Computer Games and AI Systems2005In: Ambient Intelligence: The evolution of technology, communication and cognition towards the future of human-computer interaction / [ed] G. Riva, F. Vatalaro, F. Davide, M. Alcañiz, IOS Press, 2005, Vol. 6, p. 143-155Chapter in book (Refereed)
    Abstract [en]

    The introduction of games for benchmarking intelligent systems has a long tradition in AI (Artificial Intelligence) research. Alan Turing was one of the first to mention that a computer can be considered as intelligent if it is able to play chess. Today AI benchmarks are designed to capture difficulties that humans deal with every day. They are carried out on robots with unreliable sensors and actuators or on agents integrated in digital environments that simulate aspects of the real world. One example is given by the annually held RoboCup competitions, where robots compete in a football game but also fight for the rescue of civilians in a simulated large-scale disaster simulation. Besides these scientific events, another environment, also challenging AI, originates from the commercial computer game market. Computer games are nowadays known for their impressive graphics and sound effects. However, the latest generation of game engines shows clearly that the trend leads towards more realistic physics simulations, agent centered perception, and complex player interactions due to the rapidly increasing degrees of freedom that digital characters obtain. This new freedom requests another quality of the player’s environment, a quality of ambient intelligence that appears both plausible and in real time. This intelligence has, for example, to control more than $40$ facial muscles of digital characters while they interact with humans, but also to control a team of digital characters for the support of human players. This article emphasizes the current difference between AI systems and digital characters in commercial computer games and emphasizes the advantages that arise if shrinking the gap between them. We sketch some methods currently utilized in RoboCup and relates them to methods found in commercial computer games. We show how methods from RoboCup might contribute to game AI and improve both the performance and plausibility of its digital characters. Furthermore, we describe state-of-the-art game engines and discuss the challenge but also opportunity they are offering to AI research.

  • 17.
    Kleiner, Alexander
    University of Freiburg.
    Mapping and Exploration for Search and Rescue with Humans and Mobile Robots2009Report (Other academic)
    Abstract [en]

    Urban Search And Rescue (USAR) is a time critical task since all survivors have to be rescued within the first 72 hours. One goal in Rescue Robotics is to support emergency response by mixed-initiative teams consisting of humans and robots. Their task is to explore the disaster area rapidly while reporting victim locations and hazardous areas to a central station, which then can be utilized for planning rescue missions. To fulfill this task efficiently, humans and robots have to map disaster areas jointly while co- ordinating their search at the same time. Additionally, robots have to perform subproblems, such as victim detection and navigation, autonomously. In disaster areas these problems are extraordinarily challenging due to the unstructured environment and rough terrain. Furthermore, when communication fails, methods that are deployed under such conditions have to be decentralized, i.e. operational without a central station. In this thesis a unified approach joining human and robot resources for solving these problems is contributed. Following the vision of combined multi-robot and multi-human teamwork, core problems, such as position tracking on rough terrain, mapping by mixed teams, and decentralized team coordination with limited radio communication, are directly addressed. More specific, RFID-SLAM, a novel method for robust and efficient loop closure in large-scale environments that utilizes RFID technology for data association, is contributed. The method is capable of jointly improving multiple maps from humans and robots in a centralized and decentralized manner without requiring team members to perform loops on their routes. Thereby positions of humans are tracked by PDR (Pedestrian Dead Reckoning), and robot positions by slippage- sensitive odometry, respectively. The joint-graph emerging from these trajectories serves as an input for an iterative map optimization procedure. The introduced map representation is further utilized for solving the centralized and decentralized coordination of large rescue teams. On the one hand, a deliberate method for combined task assignment and multi-agent path planning, and on the other hand, a local search method using the memory of RFIDs for coordination, are proposed. For autonomous robot navigation on rough terrain and real-time victim detection in disaster areas an efficient method for elevation map building and a novel approach to genetic MRF (Markov Random Field) model optimization are contributed. Finally, a human in the loop architecture is presented that integrates data collected by first responders into a multi-agent system via wearable computing. In this context, the support and coordination of disaster mitigation in large-scale environments from a central-command-post-perspective are described. Methods introduced in this thesis were extensively evaluated in outdoor environments and official USAR testing arenas designed by the National Institute of Standards and Technology (NIST). Furthermore, they were an integral part of systems that won in total more than 10 times the first prize at international competitions, such as the RoboCup world championships.

  • 18.
    Kleiner, Alexander
    et al.
    University of Freiberg.
    Behrens, Nils
    University of Bremen.
    Kenn, Holger
    University of Bremen.
    Wearable Computing Meets Multiagent Systems: A Real-World Interface for the RoboCupRescue Simulation Platform2006In: First International Workshop on Agent Technology for Disaster Management at AAMAS06, 2006, p. 116-123Conference paper (Refereed)
    Abstract [en]

    One big challenge in disaster response is to get an overview over the degree of damage and to provide this information, together with optimized plans for rescue missions, back to teams in the field. Collapsing infrastructure, limited visibility due to smoke and dust, and overloaded communication lines make it nearly impossible for rescue teams to report the total situation consistently. This problem can only be solved by efficiently integrating data of many observers into a single consistent view. A Global Positioning System (GPS) device in conjunction with a communication device, and sensors or simple input methods for reporting observations, offer a realistic chance to solve the data integration problem. We propose preliminary results from a wearable computing device, acquiring disaster relevant data, such as locations of victims and blockades, and show the data integration into the RoboCupRescue Simulation platform, which is a benchmark for MAS within the RoboCup competitions. We show exemplarily how the data can consistently be integrated and how rescue missions can be optimized by solutions developed on the RoboCupRescue simulation platform. The preliminary results indicate that nowadays wearable computing technology combined with MAS technology can serve as a powerful tool for Urban Search and Rescue (USAR).

  • 19.
    Kleiner, Alexander
    et al.
    University of Freiburg.
    Brenner, Michael
    University of Freiburg.
    Bräuer, Tobias
    University of Freiburg.
    Dornhege, Christian
    University of Freiburg.
    Göbelbecker, Moritz
    University of Freiburg.
    Luber, Matthias
    University of Freiburg.
    Prediger, Johann
    University of Freiburg.
    Stückler, Joerg
    University of Freiburg.
    ResQ Freiburg: Team Description and Evaluation2004In: RoboCup 2004 (CDROM Proceedings), Team Description Paper, Rescue Simulation League, 2004Conference paper (Refereed)
    Abstract [en]

    ResQ Freiburg is the world champion of the 2004 RoboCup competition in the Rescue simulation league. RoboCupRescue is a large-scale multi-agent simulation of urban disasters where, in order to save lives and minimize damage, rescue teams must effectively cooperate despite sensing and communication limitations. To accomplish this, ResQ Freiburg introduced new methods for hierarchical path planning, death-time prediction of civilians, coordination of multi-agent city exploration, as well as an any-time rescue sequence optimization based on genetic algorithms. To evaluate the usefulness of these techniques we performed an extensive evaluation of the log files of the best participating teams in the competition. Our analysis explains the reasons for our team’s success, and thus could also provide an evaluation tool for future competitions.

  • 20.
    Kleiner, Alexander
    et al.
    University of Freiburg.
    Brenner, Michael
    University of Freiburg.
    Bräuer, Tobias
    University of Freiburg.
    Dornhege, Christian
    University of Freiburg.
    Göbelbecker, Moritz
    University of Freiburg.
    Luber, Matthias
    University of Freiburg.
    Prediger, Johann
    University of Freiburg.
    Stückler, Joerg
    University of Freiburg.
    Nebel, Bernhard
    University of Freiburg.
    ResQ Freiburg: Team Description and Evaluation2005Report (Other academic)
    Abstract [en]

    ResQ Freiburg is the world champion of the 2004 RoboCup competition in the Rescue simulation league. RoboCupRescue is a large-scale multi-agent simulation of urban disasters where, in order to save lives and minimize damage, rescue teams must effectively cooperate despite sensing and communication limitations. To accomplish this, ResQ Freiburg introduced new methods for hierarchical path planning, death-time prediction of civilians, coordination of multi-agent city exploration, as well as an any-time rescue sequence optimization based on genetic algorithms. To evaluate the usefulness of these techniques we performed an extensive evaluation of the log files of the best participating teams in the competition. Our analysis explains the reasons for our team’s success, and thus could also provide an evaluation tool for future competitions.

  • 21.
    Kleiner, Alexander
    et al.
    University of Freiburg.
    Brenner, Michael
    University of Freiburg.
    Bräuer, Tobias
    University of Freiburg.
    Dornhege, Christian
    University of Freiburg.
    Göbelbecker, Moritz
    University of Freiburg.
    Luber, Matthias
    University of Freiburg.
    Prediger, Johann
    University of Freiburg.
    Stückler, Jörg
    University of Freiburg.
    Nebel, Bernhard
    University of Freiburg.
    Successful Search and Rescue in Simulated Disaster Areas2005In: Robocup 2005: Robot Soccer World Cup IX, 2005, Vol. 4020, p. 323-334Conference paper (Refereed)
    Abstract [en]

    RoboCupRescue Simulation is a large-scale multi-agent simulation of urban disasters where, in order to save lives and minimize damage, rescue teams must effectively cooperate despite sensing and communication limitations. This paper presents the comprehensive search and rescue approach of the ResQ Freiburg team, the winner in the RoboCupRescue Simulation league at RoboCup 2004. Specific contributions include the predictions of travel costs and civilian life-time, the efficient coordination of an active disaster space exploration, as well as an any-time rescue sequence optimization based on a genetic algorithm. We compare the performances of our team and others in terms of their capability of extinguishing fires, freeing roads from debris, disaster space exploration, and civilian rescue. The evaluation is carried out with information extracted from simulation log files gathered during RoboCup 2004. Our results clearly explain the success of our team, and also confirm the scientific approaches proposed in this paper.

  • 22.
    Kleiner, Alexander
    et al.
    Universität Freiburg.
    Buchheim, T.
    Universität Stuttgart.
    A Plugin-Based Architecture for Simulation in the F2000 League2003In: In RoboCup 2003: Robot Soccer World Cup VII, 2003, Vol. 3020, p. 434-445Conference paper (Refereed)
    Abstract [en]

    Simulation has become an essential part in the development process of autonomous robotic systems. In the domain of robotics, developers often are confronted with problems like noisy sensor data, hardware malfunctions and scarce or temporarily inoperable hardware resources. A solution to most of the problems can be given by tools which allow the simulation of the application scenario in varying degrees of abstraction and the suppression of unwanted features of the domain (like e.g. sensor noise). The RoboCup scenario of autonomous mobile robots playing soccer is one such domain where the above mentioned problems typically arise.

  • 23.
    Kleiner, Alexander
    et al.
    University of Freiburg.
    Dietl, M.
    University of Freiburg.
    Nebel, Bernhard
    University of Freiburg.
    Towards a Life-Long Learning Soccer Agent2002In: In RoboCup 2002: Robot Soccer World Cup VI, 2002, Vol. 2752, p. 126-134Conference paper (Refereed)
    Abstract [en]

    One problem in robotic soccer (and in robotics in general) is to adapt skills and the overall behavior to a changing environment and to hardware improvements. We applied hierarchical reinforcement learning in an SMDP framework learning on all levels simultaneously. As our experiments show, learning simultaneously on the skill level and on the skill selection level is advantageous since it allows for a smooth adaption to a changing environment. Furthermore, the skills we trained turn also out to be quite competitive when run on the real robotic players of the players of our CS Freiburg team.

  • 24.
    Kleiner, Alexander
    et al.
    University of Freiburg.
    Dornhege, C.
    University of Freiburg.
    Operator-Assistive Mapping in Harsh Environments2009In: IEEE Int. Workshop on Safety, Security and Rescue Robotics (SSRR), IEEE , 2009, p. 1-6Conference paper (Other academic)
    Abstract [en]

    Teleoperation is a difficult task, particularly when controlling robots from an isolated operator station. In general, the operator has to solve nearly blindly the problems of mission planning, target identification, robot navigation, and robot control at the same time. The goal of the proposed system is to support teleoperated navigation with real-time mapping. We present a novel scan matching technique that re-considers data associations during the search, enabling robust pose estimation even under varying roll and pitch angle of the robot enabling mapping on rough terrain. The approach has been implemented as an embedded system and extensively tested on robot platforms designed for teleoperation in critical situations, such as bomb disposal. Furthermore, the system has been evaluated in a test maze by first responders during the Disaster City event in Texas 2008. Finally, experiments conducted within different environments show that the system yields comparably accurate maps in real-time when compared to higher sophisticated offline methods, such as Rao-Blackwellized SLAM.

  • 25.
    Kleiner, Alexander
    et al.
    University of Freiburg.
    Dornhege, C.
    University of Freiburg.
    Sun, D.
    University of Freiburg.
    Mapping Disaster Areas Jointly: RFID-Coordinated SLAM by Humans and Robots2007In: IEEE Int. Workshop on Safety, Security and Rescue Robotics (SSRR), IEEE , 2007, p. 1-6Conference paper (Refereed)
    Abstract [en]

    We consider the problem of jointly performing SLAM by humans and robots in Urban Search And Rescue (USAR) scenarios. In this context, SLAM is a challenging task. First, places are hardly re-observable by vision techniques since visibility might be affected by smoke and fire. Second, loop-closure is cumbersome due to the fact that firemen will intentionally try to avoid performing loops when facing the reality of emergency response, e.g. while they are searching for victims. Furthermore, there might be places that are only accessible to robots, making it necessary to integrate humans and robots into one team for mapping the area after a disaster. In this paper, we introduce a method for jointly correcting individual trajectories of humans and robots by utilizing RFID technology for data association. Hereby the poses of humans and robots are tracked by a PDR (Pedestrian Dead Reckoning), and slippage sensitive odometry, respectively. We conducted extensive experiments with a team of humans, and a human-robot team within a semi-outdoor environment. Results from these experiments show that the introduced method allows to improve single trajectories based on the joint graph, even if they do not contain any loop.

  • 26.
    Kleiner, Alexander
    et al.
    Carnegie Mellon University, Pittsburgh, PA, USA.
    Dornhege, Christian
    University of Freiburg.
    Mapping for the Support of First Responders in Critical Domains2011In: Journal of Intelligent and Robotic Systems, ISSN 0921-0296, E-ISSN 1573-0409, Vol. 64, no 1, p. 7-31Article in journal (Refereed)
    Abstract [en]

    In critical domains such as urban search and rescue (USAR), and bomb disposal, the deployment of teleoperated robots is essential to reduce the risk of first responder personnel. Teleoperation is a difficult task, particularly when controlling robots from an isolated safety zone. In general, the operator has to solve simultaneously the problems of mission planning, target identification, robot navigation, and robot control. We introduce a system to support teleoperated navigation with real-time mapping consisting of a two-step scan matching method that re-considers data associations during the search. The algorithm processes data from laser range finder and gyroscope only, thereby it is independent from the robot platform. Furthermore, we introduce a user-guided procedure for improving the global consistency of maps generated by the scan matcher. Globally consistent maps are computed by a graph-based maximum likelihood method that is biased by localizing crucial parts of the scan matcher trajectory on a prior given geo-tiff image. The approach has been implemented as an embedded system and extensively tested on robot platforms designed for teleoperation in critical situations, such as bomb disposal. Furthermore, the system was evaluated in a test maze by first responders during the Disaster City event in Texas 2008.

  • 27.
    Kleiner, Alexander
    et al.
    University of Freiburg.
    Dornhege, Christian
    University of Freiburg.
    Real-time Localization and Elevation Mapping within Urban Search and Rescue Scenarios2007In: Journal of Field Robotics, ISSN 1556-4967, Vol. 24, no 8-9, p. 723-745Article in journal (Refereed)
    Abstract [en]

    Urban Search And Rescue (USAR) is a time critical task. Rescue teams have to explore a large terrain within a short amount of time in order to locate survivors after a disaster. One goal in Rescue Robotics is to have a team of heterogeneous robots that explore autonomously, or partially guided by an incident commander, the disaster area. Their task is to jointly create a map of the terrain and to register victim locations, which can further be utilized by human task forces for rescue. Basically, the robots have to solve autonomously in real-time the problem of Simultaneous Localization and Mapping (SLAM), consisting of a continuous state estimation problem and a discrete data association problem. Extraordinary circumstances after a real disaster make it very hard to apply common techniques. Many of these have been developed under strong assumptions, for example, they require polygonal structures, such as typically found in office-like environments. Furthermore, most techniques are not deployable in real-time. In this paper we propose real-time solutions for localization and mapping, which all have been extensively evaluated within the test arenas of the National Institute of Standards and Technology (NIST). We specifically deal with the problems of vision-based pose tracking on tracked vehicles, the building of globally consistent maps based on a network of RFID tags, and the building of elevation maps from readings of a tilted Laser Range Finder (LRF). Our results show that these methods lead under modest computational requirements to good results within the utilized testing arenas.

  • 28.
    Kleiner, Alexander
    et al.
    University of Freiburg.
    Dornhege, Christian
    University of Freiburg.
    Kümmerle, Rainer
    University of Freiburg.
    Ruhnke, Michael
    University of Freiburg.
    Steder, Bastian
    University of Freiburg.
    Nebel, Bernhard
    University of Freiburg.
    Doherty, Patrick
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Wzorek, Mariusz
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Rudol, Piotr
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Conte, Gianpaolo
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Duranti, Simone
    Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group . Linköping University, The Institute of Technology.
    Lundström, David
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    RoboCupRescue - Robot League Team RescueRobots Freiburg (Germany)2006In: RoboCup 2006 (CDROM Proceedings), Team Description Paper, Rescue Robot League, 2006Conference paper (Refereed)
    Abstract [en]

    This paper describes the approach of the RescueRobots Freiburg team, which is a team of students from the University of Freiburg that originates from the former CS Freiburg team (RoboCupSoccer) and the ResQ Freiburg team (RoboCupRescue Simulation). Furthermore we introduce linkMAV, a micro aerial vehicle platform. Our approach covers RFID-based SLAM and exploration, autonomous detection of relevant 3D structures, visual odometry, and autonomous victim identification. Furthermore, we introduce a custom made 3D Laser Range Finder (LRF) and a novel mechanism for the active distribution of RFID tags.

  • 29.
    Kleiner, Alexander
    et al.
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Farinelli, A.
    University of Verona, Italy.
    Ramchurn, S.
    University of Southampton, UK.
    Shi, B.
    Wuhan University of Tec., China.
    Maffioletti, F.
    University of Verona, Italy.
    Reffato, R.
    University of Verona, Italy.
    RMASBench: Benchmarking Dynamic Multi-Agent Coordination in Urban Search and Rescue2013In: Proc. of the 12th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2013), The International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) , 2013, p. 1195-1196Conference paper (Refereed)
    Abstract [en]

    We propose RMASBench, a new benchmarking tool based on the RoboCup Rescue Agent simulation system, to easily compare coordination approaches in a dynamic rescue scenario. In particular, we offer simple interfaces to plug-in coordination algorithms without the need for implementing and tuning low-level agents behaviors. Moreover, we add to the realism of the simulation by providing a large scale crowd simulator, which exploits GPUs parallel architecture, to simulate the behavior of thousands of agents in real time. Finally, we focus on a specific coordination problem where fire fighters must combat fires and prevent them from spreading across the city. We formalize this problem as a Distributed Constraint Optimization Problem and we compare two state-of-the art solution techniques: DSA and MaxSum. We perform an extensive empirical evaluation of such techniques considering several standard measures for performance (e.g. damages to buildings) and coordination overhead (e.g., message exchanged and non concurrent constraint checks). Our results provide interesting insights on limitations and benefits of DSA and MaxSum in our rescue scenario and demonstrate that RMASBench offers powerful tools to compare coordination algorithms in a dynamic environment.

  • 30.
    Kleiner, Alexander
    et al.
    University of Freiburg.
    Göbelbecker, Moritz
    University of Freiburg.
    Rescue3D: Making Rescue Simulation Attractive to the Public2004Report (Other academic)
    Abstract [en]

    RoboCupRescue Simulation is a large-scale multi-agent simulation of urban disasters where, in order to save lives and minimize damage, rescue teams must effectively cooperate despite sensing and communication limitations. The annually increasing number of teams participating in this league shows clearly that there is a high demand on research in this field. However, from our experience of participating at RoboCup as a team, but also from organizing RoboCupRescue as a public event, we learned about two strong limitations that arise practically during the competition: First, the current system offers only limited methods for comparing specific abilities of rescue teams. Second, the current presentation of the competition is only limited understandable for spectators. Within our effort in developing a new visualization of the rescue domain, we want to focus on these two limitations. We introduce a system for visualization that covers the demands of both developers and spectators.

  • 31.
    Kleiner, Alexander
    et al.
    University of Freiburg.
    Kolling, A.
    School of Information Sciences, University of Pittsburgh, Pittsburgh, PA, USA.
    Lewis, M.
    School of Information Sciences, University of Pittsburgh, Pittsburgh, PA, USA.
    Sycara, K.
    Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
    Hierarchical Visibility for Guaranteed Search in Large-Scale Outdoor Terrain2013In: Autonomous Agents and Multi-Agent Systems, ISSN 1387-2532, E-ISSN 1573-7454, Vol. 26, no 1, p. 1-36Article in journal (Refereed)
    Abstract [en]

    Searching for moving targets in large environments is a challenging task that is relevant in several problem domains, such as capturing an invader in a camp, guarding security facilities, and searching for victims in large-scale search and rescue scenarios. The guaranteed search problem is to coordinate the search of a team of agents to guarantee the discovery of all targets. In this paper we present a self-contained solution to this problem in 2.5D real-world domains represented by digital elevation models (DEMs). We introduce hierarchical sampling on DEMs for selecting heuristically the close to minimal set of locations from which the entire surface of the DEM can be guarded. Locations are utilized to form a search graph on which search strategies for mobile agents are computed. For these strategies schedules are derived which include agent paths that are directly executable in the terrain. Presented experimental results demonstrate the performance of the method. The practical feasibility of our approach has been validated during a field experiment at the Gascola robot training site where teams of humans equipped with iPads successfully searched for adversarial and omniscient evaders. The field demonstration is the largest-scale implementation of a guaranteed search algorithm to date.

  • 32.
    Kleiner, Alexander
    et al.
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Kolling, Andreas
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Guaranteed Search With Large Teams of Unmanned Aerial Vehicles2013In: Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), IEEE conference proceedings, 2013, p. 2977-2983Conference paper (Refereed)
    Abstract [en]

    We consider the problem of detecting moving and evading targets by a team of coordinated unmanned aerial vehicles (UAVs) in large and complex 2D and 2.5D environments. Our approach is based on the coordination of 2D sweep lines that move through the environment to clear it from all contamination, representing the possibility of a target being located in an area, and thereby detecting all targets. The trajectories of the UAVs are implicitly given by the motion of these sweep lines and their costs are determined by the number of UAVs needed. A novel algorithm that computes low cost coordination strategies of the UAV sweep lines in simply connected polygonal environments is presented. The resulting strategies are then converted to strategies clearing multiply connected and 2.5D environments. Experiments on real and artificial elevation maps with complex visibility constraints are presented and demonstrate the feasibility and scalability of the approach. The algorithms used for the experiments are made available on a public repository.

  • 33.
    Kleiner, Alexander
    et al.
    University of Freiburg.
    Kümmerle, R.
    University of Freiburg.
    Genetic MRF Model Optimization for Real-Time Victim Detection in Search and Rescue2007In: IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), IEEE conference proceedings, 2007, p. 3025-3030Conference paper (Refereed)
    Abstract [en]

    One primary goal in rescue robotics is to deploy a team of robots for coordinated victim search after a disaster. This requires robots to perform subtasks, such as victim detection, in real-time. Human detection by computationally cheap techniques, such as color thresholding, turn out to produce a large number of false-positives. Markov Random Fields (MRFs) can be utilized to combine the local evidence of multiple weak classifiers in order to improve the detection rate. However, inference in MRFs is computational expensive. In this paper we present a novel approach for the genetic optimizing of the building process of MRF models. The genetic algorithm determines offline relevant neighborhood relations with respect to the data, which are then utilized for generating efficient MRF models from video streams during runtime. Experimental results clearly show that compared to a Support Vector Machine (SVM) based classifier, the optimized MRF models significantly reduce the false-positive rate. Furthermore, the optimized models turned out to be up to five times faster then the non-optimized ones at nearly the same detection rate.

  • 34.
    Kleiner, Alexander
    et al.
    University of Freiburg.
    Nebel, B.
    University of Freiburg.
    Ziparo, V.
    University of Freiburg.
    A Mechanism for Dynamic Ride Sharing based on Parallel Auctions2011In: 22th International Joint Conference on Artificial Intelligence (IJCAI), 2011, p. 266-272Conference paper (Refereed)
    Abstract [en]

    Car pollution is one of the major causes of green- house emissions, and traffic congestion is rapidly becoming a social plague. Dynamic Ride Sharing (DRS) systems have the potential to mitigate this problem by computing plans for car drivers, e.g. commuters, allowing them to share their rides. Ex- isting efforts in DRS are suffering from the problem that participants are abandoning the system after repeatedly failing to get a shared ride. In this paper we present an incentive compatible DRS solution based on auctions. While existing DRS systems are mainly focusing on fixed assignments that minimize the totally travelled distance, the presented approach is adaptive to individual preferences of the participants. Furthermore, our system allows to tradeoff the minimization of Vehicle Kilometers Travelled (VKT) with the overall probability of successful ride-shares, which is an important feature when bootstrapping the system. To the best of our knowledge, we are the first to present a DRS solution based on auctions using a sealed-bid second price scheme.

  • 35.
    Kleiner, Alexander
    et al.
    University of Freiburg.
    Prediger, J.
    University of Freiburg.
    Nebel, Bernhard
    University of Freiburg.
    RFID Technology-based Exploration and SLAM for Search And Rescue2006In: Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), IEEE conference proceedings, 2006, p. 4054-4059Conference paper (Refereed)
    Abstract [en]

    Robot search and rescue is a time critical task, i.e. a large terrain has to be explored by multiple robots within a short amount of time. The efficiency of exploration depends mainly on the coordination between the robots and hence on the reliability of communication, which considerably suffers under the hostile conditions encountered after a disaster. Furthermore, rescue robots have to generate a map of the environment which has to be sufficiently accurate for reporting the locations of victims to human task forces. Basically, the robots have to solve autonomously in real-time the problem of Simultaneous Localization and Mapping (SLAM). This paper proposes a novel method for real-time exploration and SLAM based on RFID tags that are autonomously distributed in the environment. We utilized the algorithm of Lu and Milios for calculating globally consistent maps from detected RFID tags. Furthermore we show how RFID tags can be used for coordinating the exploration of multiple robots. Results from experiments conducted in the simulation and on a robot show that our approach allows the computationally efficient construction of a map within harsh environments, and coordinated exploration of a team of robots.

  • 36.
    Kleiner, Alexander
    et al.
    University of Freiburg.
    Scrapper, Chris
    The MITRE Corporation, McLean, VA, USA.
    Jacoff, Adam
    National Institute of Standards and Technology, Gaithersburg, MD, USA.
    RoboCupRescue Interleague Challenge 2009: Bridging the gap between Simulation and Reality2009In: In Proc. of the Int. Workshop on Performance Metrics for Intelligent Systems (PerMIS), 2009, p. 123-129Conference paper (Refereed)
    Abstract [en]

    The RoboCupRescue initiative, represented by real-robot and simulation league, is designed to foster the research and development of innovative technologies and assistive capabilities to help responders mitigate an emergency response situation. This competition model employed by the RobocupRescue community has proven to be a propitious model, not only for fostering the development of innovative technologies but in the development of test methods used to quantitatively evaluate the performance of these technologies. The Interleague Challenge has been initiated to evaluate real-world performance of algorithms developed in simulation, as well as to drive the development of a common interface to simplify the entry of newcomer teams to the robot league. This paper will discuss the development of emerging test methods used to evaluate robotic-mapping, the development of a common robotic platform, and the development of a novel map evaluation methodology deployed during the RoboCupRescue competition 2009.

  • 37.
    Kleiner, Alexander
    et al.
    Staffordshire University.
    Sharp, Bernadette
    Staffordshire University.
    A New Algorithm for Learning Bayesian Classifiers from Data2000In: Artificial Intelligence and Soft Computing, 2000, p. 191-197Conference paper (Refereed)
    Abstract [en]

    We introduce a new algorithm for the induction of classifiers from data, based on Bayesian networks. Basically this problem has already been examined from two perspectives: first, the induction of classifiers by learning algorithms for Bayesian networks, second, the induction of classifiers based on the naive Bayesian classifier. Our approach is located between these two perspectives; it eliminates the disadvantages of both while exploiting their advantages. In contrast to recently appeared refinements of the naive Bayes classifier, which captures single correlations in the data, we have developed an approach which captures multiple correlations and furthermore does a trade-off between complexity and accuracy. In this paper we evaluate the implementation of our approach with data sets from the machine learning repository and data sets artificially generated by Bayesian networks.

  • 38.
    Kleiner, Alexander
    et al.
    Staffordshire University.
    Sharp, Bernadette
    Staffordshire University.
    Bittel, Oliver
    University of Applied Sciences, Konstanz.
    Self Organising Maps for Value Estimation to Solve Reinforcement Learning Tasks2000In: Proc. of the 2nd International Conference on Enterprise Information Systems (ICEIS 2000), 2000, p. 74-83Conference paper (Refereed)
    Abstract [en]

    Reinforcement learning has been applied recently more and more for the optimisation of agent behaviours. This approach became popular due to its adaptive and unsupervised learning process. One of the key ideas of this approach is to estimate the value of agent states. For huge state spaces however, it is difficult to implement this approach. As a result, various models were proposed which make use of function approximators, such as neural networks, to solve this problem. This paper focuses on an implementation of value estimation with a particular class of neural networks, known as self organizing maps. Experiments with an agent moving in a gridworld and the autonomous robot Khepera have been carried out to show the benefit of our approach. The results clearly show that the conventional approach, done by an implementation of a look-up table to represent the value function, can be out performed in terms of memory usage and convergence speed.

  • 39.
    Kleiner, Alexander
    et al.
    Universität Freiburg.
    Steder, B.
    Universität Freiburg.
    Dornhege, C.
    Universität Freiburg.
    Höfer, D.
    Universität Freiburg.
    Meyer-Delius, D.
    Universität Freiburg.
    Prediger, J.
    Universität Freiburg.
    Stückler, J.
    Universität Freiburg.
    Glogowski, K.
    Universität Freiburg.
    Thurner, M.
    Universität Freiburg.
    Luber, M.
    Universität Freiburg.
    Schnell, M.
    Universität Freiburg.
    Kuemmerle, R.
    Universität Freiburg.
    Burk, T.
    Universität Freiburg.
    Bräuer, T.
    Universität Freiburg.
    Nebel, B.
    Universität Freiburg.
    RoboCupRescue - Robot League Team RescueRobots Freiburg (Germany)2005In: RoboCup 2005 (CDROM Proceedings), Team Description Paper, Rescue Robot League, 2005Conference paper (Refereed)
    Abstract [en]

    This paper describes the approach of the RescueRobots Freiburg team. RescueRobots Freiburg is a team of students from the university of Freiburg, that originates from the former CS Freiburg team (RoboCupSoccer) and the ResQ Freiburg team (RoboCupRescue Simulation). Due to the high versatility of the RoboCupRescue competition we tackle the three arenas by a a twofold approach: On the one hand we want to introduce robust vehicles that can safely be teleoperated through rubble and building debris while constructing three-dimensional maps of the environment. On the other hand we want to introduce a team of autonomous robots that quickly explore a large terrain while building a two-dimensional map. This two solutions are particularly well-suited for the red and yellow arena, respectively. Our solution for the orange arena will finally be decided between these two, depending on the capabilities of both approaches at the venue. In this paper, we introduce some preliminary results that we achieved so far from map building, localization, and autonomous victim identification. Furthermore we introduce a custom made 3D Laser Range Finder (LRF) and a novel mechanism for the active distribution of RFID tags.

  • 40.
    Kleiner, Alexander
    et al.
    Albert-Ludwigs-Universität.
    Steinbauer, Gerald
    Graz University of Technology.
    Wotawa, Franz
    Graz University of Technology.
    Automated Learning of Communication Models for Robot Control Software2008In: MBS 2008 - Workshop on Model-Based Systems, 18th European Conference on Artificial Intelligence (ECAI), 2008Conference paper (Refereed)
    Abstract [en]

    Control software of autonomous mobile robots comprises a number of software modules which show very rich behaviors and interact in a very complex manner. These facts among others have a strong influence on the robustness of robot con- trol software in the field. In this paper we present an approach which is able to automatically derive a model of the structure and the behavior of the communication within a component- orientated control software. Such a model can be used for on-line model-based diagnosis in order to increase the robust- ness of the software by allowing the robot to autonomously cope with faults occurred during runtime. Due to the fact that the model is learned form recorded data and the use of the popular publisher-subscriber paradigm the approach can be applied to a wide range of complex and even partially un- known systems.

  • 41.
    Kleiner, Alexander
    et al.
    Albert-Ludwigs-Universität Freiburg.
    Steinbauer, Gerald
    Graz University of Technology.
    Wotawa, Franz
    Graz University of Technology.
    Towards Automated Online Diagnosis of Robot Navigation Software2008In: Proc. of Int. Conf. on Simulation, Modeling and Programming for Autonomous Robots (SIMPAR), Springer , 2008, Vol. 5325, p. 159-170Conference paper (Refereed)
    Abstract [en]

    Control software of autonomous mobile robots comprises a number of software modules that typically interact in a very complex way. Their proper interaction and the robustness of each single module strongly influences the safety during navigation in the field. Particularly in unstructured environments, unforeseen situations are likely to occur, causing erroneous behaviors of the robot. The proper handling of such situations requires an understanding of cause and effect within the complex interactions of the system. In this paper we present an approach which is able to automatically derive a model of the communication behavior within a component-orientated control software. The model can be used for online diagnosis in order to increase system robustness during runtime. We demonstrate model learning and system diagnosis on three different robot systems which were controlled by software modules communicating based on the widely used IPC (Inter Process Communication) standard. The demonstrated learning and diagnosis was carried out without any a priori knowledge about the systems.

  • 42.
    Kleiner, Alexander
    et al.
    University of Freiburg.
    Sun, D.
    University of Freiburg.
    Decentralized SLAM for Pedestrians without direct Communication2007In: In Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), IEEE conference proceedings, 2007, p. 1461-1466Conference paper (Refereed)
    Abstract [en]

    We consider the problem of Decentralized Simultaneous Localization And Mapping (DSLAM) for pedestrians in the context of Urban Search And Rescue (USAR). In this context, DSLAM is a challenging task. First, data exchange fails due to cut off communication links. Second, loop-closure is cumbersome due to the fact that fireman will intentionally try to avoid performing loops, when facing the reality of emergency response, e.g. while they are searching for victims. In this paper, we introduce a solution to this problem based on the non-selfish sharing of information between pedestrians for loop-closure. We introduce a novel DSLAM method which is based on data exchange and association via RFID technology, not requiring any radio communication. The approach has been evaluated within both outdoor and semi-indoor environments. The presented results show that sharing information between single pedestrians allows to optimize globally their individual paths, even if they are not able to communicate directly.

  • 43.
    Kleiner, Alexander
    et al.
    University of Freiburg.
    Sun, D.
    University of Freiburg.
    Meyer-Delius, D.
    University of Freiburg.
    ARMO - Adaptive Road Map Optimization for Large Robot Teams2011In: IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), IEEE conference proceedings, 2011, p. 3276-3282Conference paper (Refereed)
    Abstract [en]

    Autonomous robot teams that simultaneously dispatch transportation tasks are playing more and more an important role in present logistic centers and manufacturing plants. In this paper we consider the problem of robot motion planning for large robot teams in the industrial domain. We present adaptive road map optimization (ARMO) that is capable of adapting the road map in real time whenever the environment has changed. Based on linear programming, ARMO computes an optimal road map according to current environmental constraints (including human whereabouts) and the current demand for transportation tasks from loading stations in the plant. For detecting dynamic changes, the environment is describe by a grid map augmented with a hidden Markov model (HMM). We show experimentally that ARMO outperforms decoupled planning in terms of computation time and time needed for task completion.

  • 44.
    Kleiner, Alexander
    et al.
    Universität Freiburg.
    Ziparo, V. A.
    Universität Freiburg.
    RoboCupRescue - Simulation League Team RescueRobots Freiburg (Germany)2006In: RoboCup 2006 (CDROM Proceedings), Team Description Paper, Rescue Simulation League, 2006Conference paper (Refereed)
    Abstract [en]

    This paper describes the approach of the RescueRobots Freiburg Virtual League team. Our simulated robots are based on the two real robot types Lurker, a robot capable of climbing stairs and random stepfield, and Zerg, a lightweight and agile robot, capable of autonomously distributing RFID tags. Our approach covers a novel method for RFID-Technology based SLAM and exploration, allowing the fast and efficient coordination of a team of robots. Furthermore we utilize Petri nets for team coordination.

  • 45.
    Kolling, A.
    et al.
    University of Pittsburgh.
    Kleiner, Alexander
    University of Freiburg.
    Lewis, M.
    University of Pittsburgh.
    Sycara, K.
    Carnegie Mellon University, Pittsburgh, PA.
    Computing and Executing Strategies for Moving Target Search2011In: IEEE Int. Conf. on Robotics and Automation (ICRA), IEEE , 2011, p. 4246-4253Conference paper (Refereed)
    Abstract [en]

    We address the problem of searching for moving targets in large outdoor environments represented by height maps. To solve the problem we present a complete system that computes from an annotated height map a graph representation and search strategies based on worst-case assumptions about all targets. These strategies are then used to compute a schedule and task assignment for all agents. We improve the graph construction from previous work and for the first time present a method that computes a schedule to minimize the execution time. For this we consider travel times of agents determined by a path planner on the height map. We demonstrate the entire system in a real environment with an area of 700,000m2 in which eight human agents search for two intruders using mobile computing devices (iPads). To the best of our knowledge this is the first demonstration of a search system applied to such a large environment.

  • 46.
    Kolling, A.
    et al.
    University of Pittsburgh.
    Kleiner, Alexander
    Carnegie Mellon University, Pittsburgh, PA.
    Lewis, M.
    University of Pittsburgh.
    Sycara, K.
    Carnegie Mellon University.
    Pursuit-Evasion in 2.5d based on Team-Visibility2010In: IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), IEEE , 2010, p. 4610-4616Conference paper (Refereed)
    Abstract [en]

    In this paper we present an approach for a pursuit-evasion problem that considers a 2.5d environment represented by a height map. Such a representation is particularly suitable for large-scale outdoor pursuit-evasion, captures some aspects of 3d visibility and can include target heights. In our approach we construct a graph representation of the environment by sampling points and computing detection sets, an extended notion of visibility. Moreover, the constructed graph captures overlaps of detection sets allowing for a coordinated team-based clearing of the environment with robots that move to the sampled points. Once a graph is constructed we compute strategies on it utilizing previous work on graph-searching. This is converted into robot paths that are planned on the height map by classifying the terrain appropriately. In experiments we investigate the performance of our approach and provide examples including a sample map with multiple loops and elevation plateaus and two realistic maps, one of a village and one of a mountain range. To the best of our knowledge the presented approach is the first viable solution to 2.5d pursuit-evasion with height maps.

  • 47.
    Kolling, A.
    et al.
    University of Pittsburgh.
    Kleiner, Alexander
    Carnegie Mellon University, Pittsburgh, PA.
    Lewis, M.
    University of Pittsburgh.
    Sycara, K.
    Carnegie Mellon University, Pittsburgh, PA.
    Solving Pursuit-Evasion Problems on Height Maps2010In: IEEE International Conference on Robotics and Automation (ICRA 2010) Workshop: Search and Pursuit/Evasion in the Physical World: Efficiency, Scalability, and Guarantees, IEEE , 2010Conference paper (Refereed)
    Abstract [en]

    In this paper we present an approach for a pursuit-evasion problem that considers a 2.5d environment represented by a height map. Such a representation is particularly suitable for large-scale outdoor pursuit-evasion. By allowing height information we not only capture some aspects of 3d visibility but can also consider target heights. In our approach we construct a graph representation of the environment by sampling points and their detection sets which extend the usual notion of visibility. Once a graph is constructed we compute strategies on this graph using a modification of previous work on graph-searching. This strategy is converted into robot paths that are planned on the height map by classifying the terrain appropriately. In experiments we investigate the performance of our approach and provide examples including a map of a small village with surrounding hills and a sample map with multiple loops and elevation plateaus. Experiments are carried out with varying sensing ranges as well as target and sensor heights. To the best of our knowledge the presented approach is the first viable solution to 2.5d pursuit-evasion with height maps.

  • 48.
    Kolling, Andreas
    et al.
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Kleiner, Alexander
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Multi-UAV Trajectory Planning for Guaranteed Search2013In: Proc. of the 12th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2013), The International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) , 2013, p. 79-86Conference paper (Refereed)
    Abstract [en]

    We consider the problem of detecting all moving and evading targets in 2.5D environments with teams of UAVs. Targets are assumed to be fast and omniscient while UAVs are only equipped with limited range detection sensors and have no prior knowledge about the location of targets. We present an algorithm that, given an elevation map of the environment, computes synchronized trajectories for the UAVs to guarantee the detection of all targets. The approach is based on coordinating the motion of multiple UAVs on sweep lines to clear the environment from contamination, which represents the possibility of an undetected target being located in an area. The goal is to compute trajectories that minimize the number of UAVs needed to execute the guaranteed search. This is achieved by converting 2D strategies, computed for a polygonal representation of the environment, to 2.5D strategies. We present methods for this conversion and consider cost of motion and visibility constraints. Experimental results demonstrate feasibility and scalability of the approach. Experiments are carried out on real and artificial elevation maps and provide the basis for future deployments of large teams of real UAVs for guaranteed search.

  • 49.
    Kolling, Andreas
    et al.
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Kleiner, Alexander
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Rudol, Piotr
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Fast Guaranteed Search With Unmanned Aerial Vehicles2013In: Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013), IEEE , 2013, p. 6013-6018Conference paper (Refereed)
    Abstract [en]

    In this paper we consider the problem of searching for an arbitrarily smart and fast evader in a large environment with a team of unmanned aerial vehicles (UAVs) while providing guarantees of detection. Our emphasis is on the fast execution of efficient search strategies that minimize the number of UAVs and the search time. We present the first approach for computing fast search strategies utilizing additional searchers to speed up the execution time and thereby enabling large scale UAV search. In order to scale to very large environments when using UAVs one would either have to overcome the energy limitations of UAVs or pay the cost of utilizing additional UAVs to speed up the search. Our approach is based on coordinating UAVs on sweep lines, covered by the UAV sensors, that move simultaneously through an environment. We present some simulation results that show a significant reduction in execution time when using multiple UAVs and a demonstration of a real system with three ARDrones. 

  • 50.
    Kümmerle, R.
    et al.
    University of Freiburg.
    Steder, B.
    University of Freiburg.
    Dornhege, C.
    University of Freiburg.
    Kleiner, Alexander
    University of Freiburg.
    Grisetti, G.
    University of Freiburg.
    Burgard, W.
    University of Freiburg.
    Large Scale Graph-based SLAM using Aerial Images as Prior Information2009In: Proceedings of Robotics Science and Systems (RSS), MIT Press , 2009Conference paper (Refereed)
    Abstract [en]

    To effectively navigate in their environments and accurately reach their target locations, mobile robots require a globally consistent map of the environment. The problem of learning a map with a mobile robot has been intensively studied in the past and is usually referred to as the simultaneous localization and mapping (SLAM) problem. However, existing solutions to the SLAM problem typically rely on loop-closures to obtain global consistency and do not exploit prior information even if it is available. In this paper, we present a novel SLAM approach that achieves global consistency by utilizing publicly accessible aerial photographs as prior information. Our approach inserts correspondences found between three-dimensional laser range scans and the aerial image as constraints into a graph-based formulation of the SLAM problem. We evaluate our algorithm based on large real-world datasets acquired in a mixed in- and outdoor environment by comparing the global accuracy with state-of-the-art SLAM approaches and GPS. The experimental results demonstrate that the maps acquired with our method show increased global consistency.

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