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  • 1.
    Andersson, Olov
    et al.
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Wzorek, Mariusz
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Rudol, Piotr
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Doherty, Patrick
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Model-Predictive Control with Stochastic Collision Avoidance using Bayesian Policy Optimization2016In: IEEE International Conference on Robotics and Automation (ICRA), 2016, Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 4597-4604Conference paper (Refereed)
    Abstract [en]

    Robots are increasingly expected to move out of the controlled environment of research labs and into populated streets and workplaces. Collision avoidance in such cluttered and dynamic environments is of increasing importance as robots gain more autonomy. However, efficient avoidance is fundamentally difficult since computing safe trajectories may require considering both dynamics and uncertainty. While heuristics are often used in practice, we take a holistic stochastic trajectory optimization perspective that merges both collision avoidance and control. We examine dynamic obstacles moving without prior coordination, like pedestrians or vehicles. We find that common stochastic simplifications lead to poor approximations when obstacle behavior is difficult to predict. We instead compute efficient approximations by drawing upon techniques from machine learning. We propose to combine policy search with model-predictive control. This allows us to use recent fast constrained model-predictive control solvers, while gaining the stochastic properties of policy-based methods. We exploit recent advances in Bayesian optimization to efficiently solve the resulting probabilistically-constrained policy optimization problems. Finally, we present a real-time implementation of an obstacle avoiding controller for a quadcopter. We demonstrate the results in simulation as well as with real flight experiments.

  • 2.
    Berger, Cyrille
    et al.
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Rudol, Piotr
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Wzorek, Mariusz
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Kleiner, Alexander
    iRobot, Pasadena, CA, USA.
    Evaluation of Reactive Obstacle Avoidance Algorithms for a Quadcopter2016In: Proceedings of the 14th International Conference on Control, Automation, Robotics and Vision 2016 (ICARCV), IEEE conference proceedings, 2016, article id Tu31.3Conference paper (Refereed)
    Abstract [en]

    In this work we are investigating reactive avoidance techniques which can be used on board of a small quadcopter and which do not require absolute localisation. We propose a local map representation which can be updated with proprioceptive sensors. The local map is centred around the robot and uses spherical coordinates to represent a point cloud. The local map is updated using a depth sensor, the Inertial Measurement Unit and a registration algorithm. We propose an extension of the Dynamic Window Approach to compute a velocity vector based on the current local map. We propose to use an OctoMap structure to compute a 2-pass A* which provide a path which is converted to a velocity vector. Both approaches are reactive as they only make use of local information. The algorithms were evaluated in a simulator which offers a realistic environment, both in terms of control and sensors. The results obtained were also validated by running the algorithms on a real platform.

  • 3.
    Conte, Gianpaolo
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Hempel, Maria
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Rudol, Piotr
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Lundström, David
    Linköping University, The Institute of Technology. Linköping University, Department of Management and Engineering, Fluid and Mechanical Engineering Systems.
    Duranti, Simone
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group .
    Wzorek, Mariusz
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Doherty, Patrick
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    High Accuracy Ground Target Geo-Location Using Autonomous Micro Aerial Vehicle Platforms2008In: Proceedings of the AIAA Guidance, Navigation, and Control Conference (GNC), AIAA , 2008Conference paper (Refereed)
    Abstract [en]

    This paper presents a method for high accuracy ground target localization using a Micro Aerial Vehicle (MAV) equipped with a video camera sensor. The proposed method is based on a satellite or aerial image registration technique. The target geo-location is calculated by registering the ground target image taken from an on-board video camera with a geo- referenced satellite image. This method does not require accurate knowledge of the aircraft position and attitude, therefore it is especially suitable for MAV platforms which do not have the capability to carry accurate sensors due to their limited payload weight and power resources.  The paper presents results of a ground target geo-location experiment based on an image registration technique. The platform used is a MAV prototype which won the 3rd US-European Micro Aerial Vehicle Competition (MAV07). In the experiment a ground object was localized with an accuracy of 2.3 meters from a ight altitude of 70 meters.

  • 4.
    Conte, Gianpaolo
    et al.
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems. Linköping University, The Institute of Technology.
    Kleiner, Alexander
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems. Linköping University, The Institute of Technology.
    Rudol, Piotr
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems. Linköping University, The Institute of Technology.
    Korwel, Karol
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems.
    Wzorek, Mariusz
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems. Linköping University, The Institute of Technology.
    Doherty, Patrick
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated 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, 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.

  • 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.
    Rudol, Piotr
    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.
    Evaluation of a Light-weight Lidar and a Photogrammetric System for Unmanned Airborne Mapping Applications: [Bewertung eines Lidar-systems mit geringem Gewicht und eines photogrammetrischen Systems für Anwendungen auf einem UAV]2014In: Photogrammetrie - Fernerkundung - Geoinformation, ISSN 1432-8364, no 4, p. 287-298Article in journal (Refereed)
    Abstract [en]

    This paper presents a comparison of two light-weight and low-cost airborne mapping systems. One is based on a lidar technology and the other on a video camera. The airborne lidar system consists of a high-precision global navigation satellite system (GNSS) receiver, a microelectromechanical system (MEMS) inertial measurement unit, a magnetic compass and a low-cost lidar scanner. The vision system is based on a consumer grade video camera. A commercial photogrammetric software package is used to process the acquired images and generate a digital surface model. The two systems are described and compared in terms of hardware requirements and data processing. The systems are also tested and compared with respect to their application on board of an unmanned aerial vehicle (UAV). An evaluation of the accuracy of the two systems is presented. Additionally, the multi echo capability of the lidar sensor is evaluated in a test site covered with dense vegetation. The lidar and the camera systems were mounted and tested on-board an industrial unmanned helicopter with maximum take-off weight of around 100 kilograms. The presented results are based on real flight-test data.

  • 6.
    Danelljan, Martin
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Granström, Karl
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Heintz, Fredrik
    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.
    Wzorek, Mariusz
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Kvarnström, Jonas
    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.
    A Low-Level Active Vision Framework for Collaborative Unmanned Aircraft Systems2015In: COMPUTER VISION - ECCV 2014 WORKSHOPS, PT I / [ed] Lourdes Agapito, Michael M. Bronstein and Carsten Rother, Springer Publishing Company, 2015, Vol. 8925, p. 223-237Conference paper (Refereed)
    Abstract [en]

    Micro unmanned aerial vehicles are becoming increasingly interesting for aiding and collaborating with human agents in myriads of applications, but in particular they are useful for monitoring inaccessible or dangerous areas. In order to interact with and monitor humans, these systems need robust and real-time computer vision subsystems that allow to detect and follow persons.

    In this work, we propose a low-level active vision framework to accomplish these challenging tasks. Based on the LinkQuad platform, we present a system study that implements the detection and tracking of people under fully autonomous flight conditions, keeping the vehicle within a certain distance of a person. The framework integrates state-of-the-art methods from visual detection and tracking, Bayesian filtering, and AI-based control. The results from our experiments clearly suggest that the proposed framework performs real-time detection and tracking of persons in complex scenarios

  • 7.
    Doherty, Patrick
    et al.
    Linköping University, Faculty of Science & Engineering. Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems.
    Kvarnström, Jonas
    Linköping University, Faculty of Science & Engineering. Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems.
    Rudol, Piotr
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Wzorek, Mariusz
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Conte, Gianpaolo
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Berger, Cyrille
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Hinzmann, Timo
    Stastny, Thomas
    A Collaborative Framework for 3D Mapping using Unmanned Aerial Vehicles2016In: PRIMA 2016: Principles and Practice of Multi-Agent Systems / [ed] Baldoni, M., Chopra, A.K., Son, T.C., Hirayama, K., Torroni, P., Springer Publishing Company, 2016, p. 110-130Conference paper (Refereed)
    Abstract [en]

    This paper describes an overview of a generic framework for collaboration among humans and multiple heterogeneous robotic systems based on the use of a formal characterization of delegation as a speech act. The system used contains a complex set of integrated software modules that include delegation managers for each platform, a task specification language for characterizing distributed tasks, a task planner, a multi-agent scan trajectory generation and region partitioning module, and a system infrastructure used to distributively instantiate any number of robotic systems and user interfaces in a collaborative team. The application focusses on 3D reconstruction in alpine environments intended to be used by alpine rescue teams. Two complex UAV systems used in the experiments are described. A fully autonomous collaborative mission executed in the Italian Alps using the framework is also described.

  • 8.
    Doherty, Patrick
    et al.
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems. Linköping University, The Institute of Technology.
    Kvarnström, Jonas
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems. Linköping University, The Institute of Technology.
    Wzorek, Mariusz
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems. Linköping University, The Institute of Technology.
    Rudol, Piotr
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems. Linköping University, The Institute of Technology.
    Heintz, Fredrik
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems. Linköping University, The Institute of Technology.
    Conte, Gianpaolo
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems. Linköping University, The Institute of Technology.
    HDRC3 - A Distributed Hybrid Deliberative/Reactive Architecture for Unmanned Aircraft Systems2014In: Handbook of Unmanned Aerial Vehicles / [ed] Kimon P. Valavanis, George J. Vachtsevanos, Dordrecht: Springer Science+Business Media B.V., 2014, p. 849-952Chapter in book (Other academic)
    Abstract [en]

    This chapter presents a distributed architecture for unmanned aircraft systems that provides full integration of both low autonomy and high autonomy. The architecture has been instantiated and used in a rotorbased aerial vehicle, but is not limited to use in particular aircraft systems. Various generic functionalities essential to the integration of both low autonomy and high autonomy in a single system are isolated and described. The architecture has also been extended for use with multi-platform systems. The chapter covers the full spectrum of functionalities required for operation in missions requiring high autonomy.  A control kernel is presented with diverse flight modes integrated with a navigation subsystem. Specific interfaces and languages are introduced which provide seamless transition between deliberative and reactive capability and reactive and control capability. Hierarchical Concurrent State Machines are introduced as a real-time mechanism for specifying and executing low-level reactive control. Task Specification Trees are introduced as both a declarative and procedural mechanism for specification of high-level tasks. Task planners and motion planners are described which are tightly integrated into the architecture. Generic middleware capability for specifying data and knowledge flow within the architecture based on a stream abstraction is also described. The use of temporal logic is prevalent and is used both as a specification language and as an integral part of an execution monitoring mechanism. Emphasis is placed on the robust integration and interaction between these diverse functionalities using a principled architectural framework.  The architecture has been empirically tested in several complex missions, some of which are described in the chapter.

  • 9.
    Doherty, Patrick
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Rudol, Piotr
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    A UAV search and rescue scenario with human body detection and geolocalization2007In: Proceedings of the 20th Australian Joint Conference on Artificial Intelligence (AI), Springer Berlin/Heidelberg, 2007Conference paper (Refereed)
    Abstract [en]

    The use of Unmanned Aerial Vehicles (UAVs) which can operate autonomously in dynamic and complex operational environments is becoming increasingly more common. The UAVTech Lab, is pursuing a long term research endeavour related to the development of future aviation systems which try and push the envelope in terms of using and integrating high-level deliberative or AI functionality with traditional reactive and control components in autonomous UAV systems. In order to carry on such research, one requires challenging mission scenarios which force such integration and development. In this paper, one of these challenging emergency services mission scenarios is presented. It involves search and rescue for injured civilians by UAVs. In leg I of the mission, UAVs scan designated areas and try to identify injured civilians. In leg II of the mission, an attempt is made to deliver medical and other supplies to identified victims. We show how far we have come in implementing and executing such a challenging mission in realistic urban scenarios.

  • 10.
    Duranti, Simone
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group .
    Conte, Gianpaolo
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Lundström, David
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group .
    Rudol, Piotr
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Wzorek, Mariusz
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Doherty, Patrick
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    LinkMAV, a prototype rotary wing micro aerial vehicle.2007In: 17th IFAC Symposium on Automatic Control in Aerospace,2007, Oxford: Elsevier , 2007Conference paper (Refereed)
  • 11.
    Heintz, Fredrik
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Rudol, Piotr
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Doherty, Patrick
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Bridging the Sense-Reasoning Gap Using DyKnow: A Knowledge Processing Middleware Framework2007In: Proceedings of the 30th Annual German Conference on Artificial Intelligence (KI) / [ed] Joachim Hertzberg, Michael Beetz and Roman Englert, Berlin, Heidelberg: Springer , 2007, p. 460-463Conference paper (Refereed)
    Abstract [en]

    To achieve complex missions an autonomous unmanned aerial vehicle (UAV) operating in dynamic environments must have and maintain situational awareness. This can be achieved by continually gathering information from many sources, selecting the relevant information for current tasks, and deriving models about the environment and the UAV itself. It is often the case models suitable for traditional control, are not sufficient for deliberation. The need for more abstract models creates a sense-reasoning gap. This paper presents DyKnow, a knowledge processing middleware framework, and shows how it supports bridging the gap in a concrete UAV traffic monitoring application. In the example, sequences of color and thermal images are used to construct and maintain qualitative object structures. They model the parts of the environment necessary to recognize traffic behavior of tracked vehicles in real-time. The system has been implemented and tested in simulation and on data collected during flight tests.

  • 12.
    Heintz, Fredrik
    et al.
    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.
    Doherty, Patrick
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    From Images to Traffic Behavior - A UAV Tracking and Monitoring Application2007In: Proceedings of the 10th International Conference on Information Fusion (FUSION), IEEE conference proceedings , 2007Conference paper (Refereed)
    Abstract [en]

    An implemented system for achieving high level situation awareness about traffic situations in an urban area is described. It takes as input sequences of color and thermal images which are used to construct and maintain qualitative object structures and to recognize the traffic behavior of the tracked vehicles in real time. The system is tested both in simulation and on data collected during test flights. To facilitate the signal to symbol transformation and the easy integration of the streams of data from the sensors with the GIS and the chronicle recognition system, DyKnow, a stream-based knowledge processing middleware, is used. It handles the processing of streams, including the temporal aspects of merging and synchronizing streams, and provides suitable abstractions to allow high level reasoning and narrow the sense reasoning gap.

  • 13.
    Häger, Gustav
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Bhat, Goutam
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Danelljan, Martin
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Khan, Fahad Shahbaz
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Felsberg, Michael
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Rudol, Piotr
    Linköping University, The Institute of Technology.
    Doherty, Patrick
    Linköping University, The Institute of Technology.
    Combining Visual Tracking and Person Detection for Long Term Tracking on a UAV2016In: Proceedings of the 12th International Symposium on Advances in Visual Computing, 2016Conference paper (Refereed)
    Abstract [en]

    Visual object tracking performance has improved significantly in recent years. Most trackers are based on either of two paradigms: online learning of an appearance model or the use of a pre-trained object detector. Methods based on online learning provide high accuracy, but are prone to model drift. The model drift occurs when the tracker fails to correctly estimate the tracked object’s position. Methods based on a detector on the other hand typically have good long-term robustness, but reduced accuracy compared to online methods.

    Despite the complementarity of the aforementioned approaches, the problem of fusing them into a single framework is largely unexplored. In this paper, we propose a novel fusion between an online tracker and a pre-trained detector for tracking humans from a UAV. The system operates at real-time on a UAV platform. In addition we present a novel dataset for long-term tracking in a UAV setting, that includes scenarios that are typically not well represented in standard visual tracking datasets.

  • 14.
    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.

  • 15.
    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. 

  • 16.
    Merz, Torsten
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group .
    Rudol, Piotr
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Wzorek, Mariusz
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Control System Framework for Autonomous Robots Based on Extended State Machines2006In: Proceedings of the International Conference on Autonomic and Autonomous Systems (ICAS), 2006Conference paper (Refereed)
  • 17.
    Pandzic, Igor S.
    et al.
    Zagreb University Faculty of electrical engineering and computing.
    Ahlberg, Jörgen
    Visage Technologies AB.
    Wzorek, Mariusz
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Rudol, Piotr
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Mosmondor, Miran
    Zagreb University Faculty of electrical engineering and computing.
    Faces Everywhere: Towards Ubiquitous Production and Delivery of Face Animation2003In: Proceedings of the 2nd International Conference on Mobile and Ubiquitous Multimedia (MUM), Linköping: Linköping University Electronic Press, 2003, p. 49-56Conference paper (Refereed)
    Abstract [en]

    While face animation is still considered one of the toughesttasks in computer animation, its potential application range israpidly moving from the classical field of film production intogames, communications, news delivery and commerce. Tosupport such novel applications, it is important to enableproduction and delivery of face animation on a wide range ofplatforms, from high-end animation systems to the web, gameconsoles and mobile phones. Our goal is to offer a frameworkof tools interconnected by standard formats and protocols andcapable of supporting any imaginable application involvingface animation with the desired level of animation quality,automatic production wherever it is possible, and delivery ona wide range of platforms. While this is clearly an ongoingtask, we present the current state of development along withseveral case studies showing that a wide range of applicationsis already enabled.

  • 18.
    Rudol, Piotr
    Linköping University, Department of Computer and Information Science, UASTECH - Autonomous Unmanned Aircraft Systems Technologies. Linköping University, The Institute of Technology.
    Increasing Autonomy of Unmanned Aircraft Systems Through the Use of Imaging Sensors2011Licentiate thesis, monograph (Other academic)
    Abstract [en]

    The range of missions performed by Unmanned Aircraft Systems (UAS) has been steadily growing in the past decades thanks to continued development in several disciplines. The goal of increasing the autonomy of UAS's is widening the range of tasks which can be carried out without, or with minimal, external help. This thesis presents methods for increasing specific aspects of autonomy of UAS's operating both in outdoor and indoor environments where cameras are used as the primary sensors.

    First, a method for fusing color and thermal images for object detection, geolocation and tracking for UAS's operating primarily outdoors is presented. Specifically, a method for building saliency maps where human body locations are marked as points of interest is described. Such maps can be used in emergency situations to increase the situational awareness of first responders or a robotic system itself. Additionally, the same method is applied to the problem of vehicle tracking. A generated stream of geographical locations of tracked vehicles increases situational awareness by allowing for qualitative reasoning about, for example, vehicles overtaking, entering or leaving crossings.

    Second, two approaches to the UAS indoor localization problem in the absence of GPS-based positioning are presented. Both use cameras as the main sensors and enable autonomous indoor ight and navigation. The first approach takes advantage of cooperation with a ground robot to provide a UAS with its localization information. The second approach uses marker-based visual pose estimation where all computations are done onboard a small-scale aircraft which additionally increases its autonomy by not relying on external computational power.

  • 19.
    Rudol, Piotr
    et al.
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Doherty, Patrick
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Human Body Detection and Geolocalization for UAV Search and Rescue Missions Using Color and Thermal Imagery2008In: Proceedings of the IEEE Aerospace Conference, IEEE , 2008, p. 1-8Conference paper (Refereed)
    Abstract [en]

    Recent advances in the field of Unmanned Aerial Vehicles (UAVs) make flying robots suitable platforms for carrying sensors and computer systems capable of performing advanced tasks. This paper presents a technique which allows detecting humans at a high frame rate on standard hardware onboard an autonomous UAV in a real-world outdoor environment using thermal and color imagery. Detected human positions are geolocated and a map of points of interest is built. Such a saliency map can, for example, be used to plan medical supply delivery during a disaster relief effort. The technique has been implemented and tested on-board the UAVTech1 autonomous unmanned helicopter platform as a part of a complete autonomous mission. The results of flight- tests are presented and performance and limitations of the technique are discussed.

  • 20.
    Rudol, Piotr
    et al.
    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.
    Conte, Gianpaolo
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Doherty, Patrick
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Micro unmanned aerial vehicle visual servoing for cooperative indoor exploration2008In: Proceedings of the IEEE Aerospace Conference, IEEE conference proceedings , 2008Conference paper (Refereed)
    Abstract [en]

    Recent advances in the field of micro unmanned aerial vehicles (MAVs) make flying robots of small dimensions suitable platforms for performing advanced indoor missions. In order to achieve autonomous indoor flight a pose estimation technique is necessary. This paper presents a complete system which incorporates a vision-based pose estimation method to allow a MAV to navigate in indoor environments in cooperation with a ground robot. The pose estimation technique uses a lightweight light emitting diode (LED) cube structure as a pattern attached to a MAV. The pattern is observed by a ground robot's camera which provides the flying robot with the estimate of its pose. The system is not confined to a single location and allows for cooperative exploration of unknown environments. It is suitable for performing missions of a search and rescue nature where a MAV extends the range of sensors of the ground robot. The performance of the pose estimation technique and the complete system is presented and experimental flights of a vertical take-off and landing (VTOL) MAV are described.

  • 21.
    Rudol, Piotr
    et al.
    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, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group . Linköping University, The Institute of Technology.
    Doherty, Patrick
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Vision-based Pose Estimation for Autonomous Indoor Navigation of Micro-scale Unmanned Aircraft Systems2010In: Proceedings of the 2010 IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings , 2010, p. 1913-1920Conference paper (Refereed)
    Abstract [en]

    We present a navigation system for autonomous indoor flight of micro-scale Unmanned Aircraft Systems (UAS) which is based on a method for accurate monocular vision pose estimation. The method makes use of low cost artificial landmarks placed in the environment and allows for fully autonomous flight with all computation done on-board a UAS on COTS hardware. We provide a detailed description of all system components along with an accuracy evaluation and a time profiling result for the pose estimation method. Additionally, we show how the system is integrated with an existing micro-scale UAS and provide results of experimental autonomous flight tests. To our knowledge, this system is one of the first to allow for complete closed-loop control and goal-driven navigation of a micro-scale UAS in an indoor setting without requiring connection to any external entities.

  • 22.
    Wzorek, Mariusz
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Conte, Gianpaolo
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Rudol, Piotr
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Merz, Torsten
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group .
    Duranti, Simone
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group .
    Doherty, Patrick
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    From Motion Planning to Control - A Navigation Framework for an Autonomous Unmanned Aerial Vehicle2006In: Proceedings of the 21st Bristol UAV Systems Conference (UAVS), 2006Conference paper (Refereed)
    Abstract [en]

    The use of Unmanned Aerial Vehicles (UAVs) which can operate autonomously in dynamic and complex operational environments is becoming increasingly more common. While the application domains in which they are currently used are still predominantly military in nature, in the future we can expect wide spread usage in thecivil and commercial sectors. In order to insert such vehicles into commercial airspace, it is inherently important that these vehicles can generate collision-free motion plans and also be able to modify such plans during theirexecution in order to deal with contingencies which arise during the course of operation. In this paper, wepresent a fully deployed autonomous unmanned aerial vehicle, based on a Yamaha RMAX helicopter, whichis capable of navigation in urban environments. We describe a motion planning framework which integrates two sample-based motion planning techniques, Probabilistic Roadmaps and Rapidly Exploring Random Treestogether with a path following controller that is used during path execution. Integrating deliberative services, suchas planners, seamlessly with control components in autonomous architectures is currently one of the major open problems in robotics research. We show how the integration between the motion planning framework and thecontrol kernel is done in our system.

    Additionally, we incorporate a dynamic path reconfigurability scheme. It offers a surprisingly efficient method for dynamic replanning of a motion plan based on unforeseen contingencies which may arise during the execution of a plan. Those contingencies can be inserted via ground operator/UAV interaction to dynamically change UAV flight paths on the fly. The system has been verified through simulation and in actual flight. We present empirical results of the performance of the framework and the path following controller.

1 - 22 of 22
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