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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.
    Doherty, Patrick
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Deep Learning Quadcopter Control via Risk-Aware Active Learning2017In: Proceedings of The Thirty-first AAAI Conference on Artificial Intelligence (AAAI) / [ed] Satinder Singh and Shaul Markovitch, AAAI Press, 2017, Vol. 5, p. 3812-3818Conference paper (Refereed)
    Abstract [en]

    Modern optimization-based approaches to control increasingly allow automatic generation of complex behavior from only a model and an objective. Recent years has seen growing interest in fast solvers to also allow real-time operation on robots, but the computational cost of such trajectory optimization remains prohibitive for many applications. In this paper we examine a novel deep neural network approximation and validate it on a safe navigation problem with a real nano-quadcopter. As the risk of costly failures is a major concern with real robots, we propose a risk-aware resampling technique. Contrary to prior work this active learning approach is easy to use with existing solvers for trajectory optimization, as well as deep learning. We demonstrate the efficacy of the approach on a difficult collision avoidance problem with non-cooperative moving obstacles. Our findings indicate that the resulting neural network approximations are least 50 times faster than the trajectory optimizer while still satisfying the safety requirements. We demonstrate the potential of the approach by implementing a synthesized deep neural network policy on the nano-quadcopter microcontroller.

  • 2.
    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.

  • 3.
    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.

  • 4.
    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.

  • 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.
    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.
    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

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  • 7.
    Doherty, Patrick
    et al.
    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.
    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.
    Hastily formed knowledge networks and distributed situation awareness for collaborative robotics2021In: Autonomous Intelligent Systems, E-ISSN 2730-616X, Vol. 1, no 1, article id 16Article in journal (Refereed)
    Abstract [en]

    In the context of collaborative robotics, distributed situation awareness is essential for  supporting collective intelligence in teams of robots and human agents where it can be used for both individual and collective decision support. This is particularly important in applications pertaining to emergency rescue and crisis management. During operational missions, data and knowledge are gathered incrementally and in different ways by heterogeneous robots and humans. We describe this as the creation of Hastily Formed Knowledge Networks (HFKNs). The focus of this paper is the specification and prototyping of a general distributed system architecture that supports the creation of HFKNs by teams of robots and humans. The information collected ranges from low-level sensor data to high-level semantic knowledge, the latter represented in part as RDF Graphs. The framework includes a synchronization protocol and associated algorithms that allow for the automatic distribution and sharing of data and knowledge between agents. This is done through the distributed synchronization of RDF Graphs shared between agents. High-level semantic queries specified in SPARQL can be used by robots and humans alike to acquire both knowledge and data content from team members. The system is empirically validated and complexity results of the proposed algorithms are provided. Additionally, a field robotics case study is described, where a 3D mapping mission has been executed using several UAVs in a collaborative emergency rescue scenario while using the full HFKN Framework.

  • 8.
    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.

  • 9.
    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.

  • 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.
    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.

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  • 12.
    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)
  • 13.
    Pandzic, Igor S.
    et al.
    Faculty of electrical engineering and computing, Zagreb University, Zagreb, Croatia.
    Ahlberg, Jörgen
    Visage Technologies AB, Linköping.
    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.
    Mosmondor, Miran
    Faculty of electrical engineering and computing, Zagreb University, Zagreb, Croatia.
    Faces Everywhere: Towards Ubiquitous Production and Delivery of Face Animation2003In: MUM 2003. Proceedings of the 2nd International Conference on Mobile and Ubiquitous Multimedia, 10–12 December, 2003, Norrköping, Sweden, 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.

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    Faces Everywhere: Towards Ubiquitous Production and Delivery of Face Animation
  • 14.
    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.

  • 15.
    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.

  • 16. Order onlineBuy this publication >>
    Wzorek, Mariusz
    Linköping University, Department of Computer and Information Science, UASTECH - Autonomous Unmanned Aircraft Systems Technologies. Linköping University, The Institute of Technology.
    Selected Aspects of Navigation and Path Planning in Unmanned Aircraft Systems2011Licentiate thesis, monograph (Other academic)
    Abstract [en]

    Unmanned aircraft systems (UASs) are an important future technology with early generations already being used in many areas of application encompassing both military and civilian domains. This thesis proposes a number of integration techniques for combining control-based navigation with more abstract path planning functionality for UASs. These techniques are empirically tested and validated using an RMAX helicopter platform used in the UASTechLab at Linköping University. Although the thesis focuses on helicopter platforms, the techniques are generic in nature and can be used in other robotic systems.

    At the control level a navigation task is executed by a set of control modes. A framework based on the abstraction of hierarchical concurrent state machines for the design and development of hybrid control systems is presented. The framework is used to specify  reactive behaviors and for sequentialisation of control modes. Selected examples of control systems deployed on UASs are presented. Collision-free paths executed at the control level are generated by path planning algorithms.We propose a path replanning framework extending the existing path planners to allow dynamic repair of flight paths when new obstacles or no-fly zones obstructing the current flight path are detected. Additionally, a novel approach to selecting the best path repair strategy based on machine learning technique is presented. A prerequisite for a safe navigation in a real-world environment is an accurate geometrical model. As a step towards building accurate 3D models onboard UASs initial work on the integration of a laser range finder with a helicopter platform is also presented.

    Combination of the techniques presented provides another step towards building comprehensive and robust navigation systems for future UASs.

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    Selected Aspects of Navigation and Path Planning in Unmanned Aircraft Systems
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    omslag
  • 17. Order onlineBuy this publication >>
    Wzorek, Mariusz
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Selected Functionalities for Autonomous Intelligent Systems in Public Safety Scenarios2023Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The public safety and security application domain is an important research area that provides great benefits to society. Within this application domain, governmental and non‐governmental agencies, such as blue light organizations (e.g., police or firefighters), are often tasked with essential life‐saving activities when responding to fallouts of natural or man‐made disasters, such as earthquakes, floods, or hurricanes. 

    Recent technological advances in artificial intelligence and robotics offer novel tools that first responder teams can use to shorten response times and improve the effectiveness of rescue efforts. Modern first responder teams are increasingly being supported by autonomous intelligent systems such as ground robots or Unmanned Aerial Vehicles (UAVs). However, even though many commercial systems are available and used in real deployments, many important research questions still need to be answered. These relate to both autonomous intelligent system design and development in addition to how such systems can be used in the context of public safety applications. 

    This thesis presents a collection of functionalities for autonomous intelligent systems in public safety scenarios. Contributions in this thesis are divided into two parts. In Part 1, we focus on the design of navigation frameworks for UAVs for solving the problem of autonomous navigation in dynamic or changing environments. In particular, we present several novel ideas for integrating motion planning, control, and perception functionalities within robotic architectures to solve navigation tasks efficiently. 

    In Part 2, we concentrate on an important service that autonomous intelligent systems can offer to first responder teams. Specifically, we focus on base functionalities required for UAV‐based rapid ad hoc communication infrastructure deployment in the initial phases of rescue operations. The main idea is to use heterogeneous teams of UAVs to deploy communication nodes that include routers and are used to establish ad hoc Wireless Mesh Networks (WMNs). We consider fundamental problems related to WMN network design, such as calculating node placements, and propose efficient novel algorithms to solve these problems. 

    Considerable effort has been put into applying the developed techniques in real systems and scenarios. Thus, the approaches presented in this thesis have been validated through extensive simulations and real‐world experimentation with various UAV systems. Several contributions presented in the thesis are generic and can be adapted to other autonomous intelligent system types and application domains other than public safety and security. 

    List of papers
    1. Reconfigurable Path Planning for an Autonomous Unmanned Aerial Vehicle
    Open this publication in new window or tab >>Reconfigurable Path Planning for an Autonomous Unmanned Aerial Vehicle
    2006 (English)In: Proceedings of the Sixteenth International Conference on Automated Planning and Scheduling (ICAPS) / [ed] Derek Long, Stephen F. Smith, Daniel Borrajo, Lee McCluskey, AAAI Press, 2006, p. 438-441Conference paper, Published paper (Refereed)
    Abstract [en]

    In this paper, we present a motion planning framework for a fully deployed autonomous unmanned aerial vehicle which integrates two sample-based motion planning techniques, Probabilistic Roadmaps and Rapidly Exploring Random Trees. Additionally, we incorporate dynamic reconfigurability into the framework by integrating the motion planners with the control kernel of the UAV in a novel manner with little modification to the original algorithms. The framework has been verified through simulation and in actual flight. Empirical results show that these techniques used with such a framework offer a surprisingly efficient method for dynamically reconfiguring a motion plan based on unforeseen contingencies which may arise during the execution of a plan. The framework is generic and can be used for additional platforms.

    Place, publisher, year, edition, pages
    AAAI Press, 2006
    National Category
    Computer Sciences
    Identifiers
    urn:nbn:se:liu:diva-36789 (URN)32589 (Local ID)978-1-57735-270-9 (ISBN)32589 (Archive number)32589 (OAI)
    Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2023-05-25
    2. From Motion Planning to Control - A Navigation Framework for an Autonomous Unmanned Aerial Vehicle
    Open this publication in new window or tab >>From Motion Planning to Control - A Navigation Framework for an Autonomous Unmanned Aerial Vehicle
    Show others...
    2006 (English)In: Proceedings of the 21st Bristol UAV Systems Conference (UAVS), 2006Conference paper, Published 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.

    National Category
    Computer Sciences
    Identifiers
    urn:nbn:se:liu:diva-36792 (URN)32592 (Local ID)32592 (Archive number)32592 (OAI)
    Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2023-05-25Bibliographically approved
    3. Choosing Path Replanning Strategies for Unmanned Aircraft Systems
    Open this publication in new window or tab >>Choosing Path Replanning Strategies for Unmanned Aircraft Systems
    2010 (English)In: Proceedings of the Twentieth International Conference on Automated Planning and Scheduling (ICAPS) / [ed] Ronen Brafman, Héctor Geffner, Jörg Hoffmann, Henry Kautz, Toronto, Canada: AAAI Press , 2010, p. 193-200Conference paper, Published paper (Refereed)
    Abstract [en]

    Unmanned aircraft systems use a variety of techniques to plan collision-free flight paths given a map of obstacles and no- fly zones. However, maps are not perfect and obstacles may change over time or be detected during flight, which may in- validate paths that the aircraft is already following. Thus, dynamic in-flight replanning is required.Numerous strategies can be used for replanning, where the time requirements and the plan quality associated with each strategy depend on the environment around the original flight path. In this paper, we investigate the use of machine learn- ing techniques, in particular support vector machines, to choose the best possible replanning strategy depending on the amount of time available. The system has been implemented, integrated and tested in hardware-in-the-loop simulation with a Yamaha RMAX helicopter platform.

    Place, publisher, year, edition, pages
    Toronto, Canada: AAAI Press, 2010
    Keywords
    artificial intelligence, path planning, motion planning, machine learning, autonomous unmanned vehicles, autonomous aircraft systems
    National Category
    Computer Sciences
    Identifiers
    urn:nbn:se:liu:diva-59986 (URN)978-1-57735-449-9 (ISBN)
    Conference
    International Conference on Automated Planning and Scheduling (ICAPS)
    Projects
    Strategic Research Center MOVIIILinnaeus Center CADICSthe Center for Industrial Information Technology CENIITthe ELLIIT network for Information and Communication TechnologyLinkLabSwedish Research Council VR
    Available from: 2010-10-01 Created: 2010-10-01 Last updated: 2023-05-25
    4. A Framework for Safe Navigation of Unmanned Aerial Vehicles in Unknown Environments
    Open this publication in new window or tab >>A Framework for Safe Navigation of Unmanned Aerial Vehicles in Unknown Environments
    2017 (English)In: 2017 25TH INTERNATIONAL CONFERENCE ON SYSTEMS ENGINEERING (ICSENG), IEEE , 2017, p. 11-20Conference paper, Published paper (Refereed)
    Abstract [en]

    This paper presents a software framework which combines reactive collision avoidance control approach with path planning techniques for the purpose of safe navigation of multiple Unmanned Aerial Vehicles (UAVs) operating in unknown environments. The system proposed leverages advantages of using a fast local sense-and-react type control which guarantees real-time execution with computationally demanding path planning algorithms which generate globally optimal plans. A number of probabilistic path planning algorithms based on Probabilistic Roadmaps and Rapidly-Exploring Random Trees have been integrated. Additionally, the system uses a reactive controller based on Optimal Reciprocal Collision Avoidance (ORCA) for path execution and fast sense-and-avoid behavior. During the mission execution a 3D map representation of the environment is build incrementally and used for path planning. A prototype implementation on a small scale quad-rotor platform has been developed. The UAV used in the experiments was equipped with a structured-light depth sensor to obtain information about the environment in form of occupancy grid map. The system has been tested in a number of simulated missions as well as in real flights and the results of the evaluations are presented.

    Place, publisher, year, edition, pages
    IEEE, 2017
    National Category
    Robotics
    Identifiers
    urn:nbn:se:liu:diva-145815 (URN)10.1109/ICSEng.2017.58 (DOI)000426505200002 ()978-1-5386-0610-0 (ISBN)
    Conference
    25th International Conference on Systems Engineering (ICSEng)
    Note

    Funding Agencies|Swedish Research Council (VR) Linnaeus Center CADICS; ELLIIT network organization for Information and Communication Technology; Swedish Foundation for Strategic Research [RIT 15-0097]

    Available from: 2018-03-21 Created: 2018-03-21 Last updated: 2023-05-25
    5. LinkBoard: Advanced Flight Control System for Micro Unmanned Aerial Vehicles
    Open this publication in new window or tab >>LinkBoard: Advanced Flight Control System for Micro Unmanned Aerial Vehicles
    2017 (English)In: 2017 2ND INTERNATIONAL CONFERENCE ON CONTROL AND ROBOTICS ENGINEERING (ICCRE2017), IEEE , 2017Conference paper, Published paper (Refereed)
    Abstract [en]

    This paper presents the design and development of the LinkBoard, an advanced flight control system for micro Unmanned Aerial Vehicles (UAVs). Both hardware and software architectures are presented. The LinkBoard includes four processing units and a full inertial measurement unit. In the basic configuration, the software architecture includes a fully configurable set of control modes and sensor fusion algorithms for autonomous UAV operation. The system proposed allows for easy integration with new platforms, additional external sensors and a flexibility to trade off computational power, weight and power consumption. Due to the available onboard computational power, it has been used for computationally demanding applications such as the implementation of an autonomous indoor vision-based navigation system with all computations performed onboard. The autopilot has been manufactured and deployed on multiple UAVs. Examples of UAV systems built with the LinkBoard and their applications are presented, as well as an in-flight experimental performance evaluation of a newly developed attitude estimation filter.

    Place, publisher, year, edition, pages
    IEEE, 2017
    Keywords
    robotics; embedded systems; flight control system; unmanned aerial vehicle; attitude heading reference system
    National Category
    Embedded Systems
    Identifiers
    urn:nbn:se:liu:diva-139643 (URN)10.1109/ICCRE.2017.7935051 (DOI)000406006500021 ()978-1-5090-3774-2 (ISBN)
    Conference
    2nd International Conference on Control and Robotics Engineering (ICCRE)
    Note

    Funding Agencies|Swedish Research Council (VR) Linnaeus Center CADICS; ELLIIT network organization for Information and Communication Technology; Swedish Foundation for Strategic Research [RIT 15-0097]

    Available from: 2017-08-09 Created: 2017-08-09 Last updated: 2023-05-25
    6. Deployment of Ad Hoc Network Nodes Using UAVs for Search and Rescue Missions
    Open this publication in new window or tab >>Deployment of Ad Hoc Network Nodes Using UAVs for Search and Rescue Missions
    2018 (English)In: 2018 6TH INTERNATIONAL ELECTRICAL ENGINEERING CONGRESS (IEECON), IEEE , 2018Conference paper, Published paper (Refereed)
    Abstract [en]

    Due to the maturity of technological development, widespread use of Unmanned Aerial Vehicles (UAVs) is becoming prevalent in the civil and commercial sectors. One promising area of application is in emergency rescue support. As recently seen in a number of natural catastrophes such as the hurricanes in Texas, Florida and Puerto Rico, major communication and electrical infrastructure is knocked out, leading to an inability to communicate between the victims and rescuers on the ground as well as between rescuers themselves. This paper studies the feasibility of using heterogeneous teams of UAVs to rapidly deliver and establish ad hoc communication networks in operational environments through autonomous in-air delivery of CommKits that serve as nodes in local ad hoc networks. Hardware and software infrastructures for autonomous CommKit delivery in addition to CommKit specification and construction is considered. The results of initial evaluation of two design alternatives for CommKits are presented based on more than 25 real flight tests in different weather conditions using a commercial small-scale UAV platform.

    Place, publisher, year, edition, pages
    IEEE, 2018
    Series
    International Electrical Engineering Congress, ISSN 2474-4697
    National Category
    Human Aspects of ICT
    Identifiers
    urn:nbn:se:liu:diva-158598 (URN)10.1109/IEECON.2018.8712230 (DOI)000470229800075 ()978-1-5386-2317-6 (ISBN)
    Conference
    6th International Electrical Engineering Congress (iEECON)
    Note

    Funding Agencies|Swedish Research Council CADICS; ELLIIT network organization for Information and Communication Technology; Swedish Foundation for Strategic Research [RIT 15-0097]

    Available from: 2019-07-03 Created: 2019-07-03 Last updated: 2023-05-25
    7. Router and gateway node placement in wireless mesh networks for emergency rescue scenarios
    Open this publication in new window or tab >>Router and gateway node placement in wireless mesh networks for emergency rescue scenarios
    2021 (English)In: Autonomous Intelligent Systems, E-ISSN 2730-616X, Vol. 1, no 1, article id 14Article in journal (Refereed) Published
    Abstract [en]

    The focus of this paper is on base functionalities required for UAV-based rapid deployment of an ad hoc communication infrastructure in the initial phases of rescue operations. The main idea is to use heterogeneous teams of UAVs to deploy communication kits that include routers, and are used in the generation of ad hoc Wireless Mesh Networks (WMN). Several fundamental problems are considered and algorithms are proposed to solve these problems. The Router Node Placement problem (RNP) and a generalization of it that takes into account additional constraints arising in actual field usage is considered first. The RNP problem tries to determine how to optimally place routers in a WMN. A new algorithm, the RRT-WMN algorithm, is proposed to solve this problem. It is based in part on a novel use of the Rapidly Exploring Random Trees (RRT) algorithm used in motion planning. A comparative empirical evaluation between the RRT-WMN algorithm and existing techniques such as the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and Particle Swarm Optimization (PSO), shows that the RRT-WMN algorithm has far better performance both in amount of time taken and regional coverage as the generalized RNP problem scales to realistic scenarios. The Gateway Node Placement Problem (GNP) tries to determine how to locate a minimal number of gateway nodes in a WMN backbone network while satisfying a number of Quality of Service (QoS) constraints.Two alternatives are proposed for solving the combined RNP-GNP problem. The first approach combines the RRT-WMN algorithm with a preexisting graph clustering algorithm. The second approach, WMNbyAreaDecomposition, proposes a novel divide-and-conquer algorithm that recursively partitions a target deployment area into a set of disjoint regions, thus creating a number of simpler RNP problems that are then solved concurrently. Both algorithms are evaluated on real-world GIS models of different size and complexity. WMNbyAreaDecomposition is shown to outperform existing algorithms using 73% to 92% fewer router nodes while at the same time satisfying all QoS requirements.

    Place, publisher, year, edition, pages
    Springer, 2021
    Keywords
    Robotics; UAV deployed ad hoc networks; Wireless Mesh Networks; Router node placement; Emergency rescue
    National Category
    Computational Mathematics
    Identifiers
    urn:nbn:se:liu:diva-189014 (URN)10.1007/s43684-021-00012-0 (DOI)
    Note

    Funding Agencies|ELLIIT network organization for Information and Communication Technology; Swedish Foundation for Strategic ResearchSwedish Foundation for Strategic Research [RIT 15-0097]; Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation;The 3rd author was also supported by an RExperts Program Grant 2020A1313030098 from the Guangdong Department of Science and Technology, China and a Sichuan Province International Science and Technology Innovation Cooperation Project Grant 2020YFH0160.

    Available from: 2022-10-07 Created: 2022-10-07 Last updated: 2023-05-25
    Download full text (pdf)
    fulltext
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    presentationsbild
  • 18.
    Wzorek, Mariusz
    et al.
    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.
    Doherty, Patrick
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering. School of Intelligent Systems and Engineering, Jinan University (Zhuhai Campus), Zhuhai, China.
    Polygon Area Decomposition Using a Compactness MetricManuscript (preprint) (Other academic)
    Abstract [en]

    In this paper, we consider the problem of partitioning a polygon into a set of connected disjoint sub-polygons, each of which covers an area of a specific size. The work is motivated by terrain covering applications in robotics, where the goal is to find a set of efficient plans for a team of heterogeneous robots to cover a given area. Within this application, solving a polygon partitioning problem is an essential stepping stone. Unlike previous work, the problem formulation proposed in this paper also considers a compactness metric of the generated sub-polygons, in addition to the area size constraints. Maximizing the compactness of sub-polygons directly influences the optimality of any generated motion plans. Consequently, this increases the efficiency with which robotic tasks can be performed within each sub-region. The proposed problem representation is based on grid cell decomposition and a potential field model that allows for the use of standard optimization techniques. A new algorithm, the AreaDecompose algorithm, is proposed to solve this problem. The algorithm includes a number of existing and new optimization techniques combined with two post-processing methods. The approach has been evaluated on a set of randomly generated polygons which are then divided using different criteria and the results have been compared with a state-of-the-art algorithm. Results show that the proposed algorithm can efficiently divide polygon regions maximizing compactness of the resulting partitions, where the sub-polygon regions are on average up to 73% more compact in comparison to existing techniques.

  • 19.
    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.

  • 20.
    Wzorek, Mariusz
    et al.
    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.
    A framework for reconfigurable path planning for autonomous unmanned aerial vehicles.2007Manuscript (preprint) (Other (popular science, discussion, etc.))
  • 21.
    Wzorek, Mariusz
    et al.
    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.
    Preliminary report: Reconfigurable path planning for an autonomous unmanned aerial vehicle2005In: Proceedings of the 24th Annual Workshop of the UK Planning and Scheduling Special Interest Group (PlanSIG), 2005Conference paper (Refereed)
  • 22.
    Wzorek, Mariusz
    et al.
    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.
    Reconfigurable Path Planning for an Autonomous Unmanned Aerial Vehicle2006In: ICHIT 2006 - International Conference on Hybrid Information Technology,2006, 2006Conference paper (Refereed)
  • 23.
    Wzorek, Mariusz
    et al.
    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.
    Reconfigurable path planning for an autonomous unmanned aerial vehicle2005In: National Swedish Workshop on Autonomous Systems, SWAR 05,2005, 2005Conference paper (Refereed)
  • 24.
    Wzorek, Mariusz
    et al.
    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.
    Reconfigurable Path Planning for an Autonomous Unmanned Aerial Vehicle2006In: Proceedings of the Sixteenth International Conference on Automated Planning and Scheduling (ICAPS) / [ed] Derek Long, Stephen F. Smith, Daniel Borrajo, Lee McCluskey, AAAI Press, 2006, p. 438-441Conference paper (Refereed)
    Abstract [en]

    In this paper, we present a motion planning framework for a fully deployed autonomous unmanned aerial vehicle which integrates two sample-based motion planning techniques, Probabilistic Roadmaps and Rapidly Exploring Random Trees. Additionally, we incorporate dynamic reconfigurability into the framework by integrating the motion planners with the control kernel of the UAV in a novel manner with little modification to the original algorithms. The framework has been verified through simulation and in actual flight. Empirical results show that these techniques used with such a framework offer a surprisingly efficient method for dynamically reconfiguring a motion plan based on unforeseen contingencies which may arise during the execution of a plan. The framework is generic and can be used for additional platforms.

  • 25.
    Wzorek, Mariusz
    et al.
    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.
    The WITAS UAV Ground System Interface Demonstration with a Focus on Motion and Task Planning2006In: Software Demonstrations at the International Conference on Automated Planning Scheduling (ICAPS-SD), 2006, p. 36-37Conference paper (Other academic)
    Abstract [en]

    The Autonomous UAV Technologies Laboratory at Linköping University, Sweden, has been developing fully autonomous rotor-based UAV systems in the mini- and micro-UAV class. Our current system design is the result of an evolutionary process based on many years of developing, testing and maintaining sophisticated UAV systems. In particular, we have used the Yamaha RMAX helicopter platform(Fig. 1) and developed a number of micro air vehicles from scratch.

  • 26.
    Wzorek, Mariusz
    et al.
    Linköping University, Department of Computer and Information Science, UASTECH - Autonomous Unmanned Aircraft Systems Technologies. Linköping University, The Institute of Technology.
    Kvarnström, Jonas
    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, Department of Computer and Information Science, UASTECH - Autonomous Unmanned Aircraft Systems Technologies. Linköping University, The Institute of Technology.
    Choosing Path Replanning Strategies for Unmanned Aircraft Systems2010In: Proceedings of the Twentieth International Conference on Automated Planning and Scheduling (ICAPS) / [ed] Ronen Brafman, Héctor Geffner, Jörg Hoffmann, Henry Kautz, Toronto, Canada: AAAI Press , 2010, p. 193-200Conference paper (Refereed)
    Abstract [en]

    Unmanned aircraft systems use a variety of techniques to plan collision-free flight paths given a map of obstacles and no- fly zones. However, maps are not perfect and obstacles may change over time or be detected during flight, which may in- validate paths that the aircraft is already following. Thus, dynamic in-flight replanning is required.Numerous strategies can be used for replanning, where the time requirements and the plan quality associated with each strategy depend on the environment around the original flight path. In this paper, we investigate the use of machine learn- ing techniques, in particular support vector machines, to choose the best possible replanning strategy depending on the amount of time available. The system has been implemented, integrated and tested in hardware-in-the-loop simulation with a Yamaha RMAX helicopter platform.

  • 27.
    Wzorek, Mariusz
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Landén, David
    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.
    GSM Technology as a Communication Media for an Autonomous Unmanned Aerial Vehicle2006In: Proceedings of the 21st Bristol International UAV Systems Conference (UAVS), Bristol: University of Bristol, Department of Aerospace engineering , 2006Conference paper (Refereed)
1 - 27 of 27
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