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Wzorek, Mariusz
Publications (10 of 27) Show all publications
Wzorek, M. (2023). Selected Functionalities for Autonomous Intelligent Systems in Public Safety Scenarios. (Doctoral dissertation). Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Selected Functionalities for Autonomous Intelligent Systems in Public Safety Scenarios
2023 (English)Doctoral 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. 

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2023. p. 69
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2322
National Category
Computer Vision and Robotics (Autonomous Systems) Computer Sciences
Identifiers
urn:nbn:se:liu:diva-194100 (URN)10.3384/9789180751964 (DOI)9789180751957 (ISBN)9789180751964 (ISBN)
Public defence
2023-06-14, Ada Lovelace, B-building, Campus Valla, Linköping, 13:15 (English)
Opponent
Supervisors
Note

Funding: This work has been supported by the ELLIIT Network Organization for Information and Communication Technology, Sweden (Project B09), and Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation, in addi‐tion to the sources already acknowledged in the individual papers.

Available from: 2023-05-25 Created: 2023-05-25 Last updated: 2023-05-26Bibliographically approved
Doherty, P., Berger, C., Rudol, P. & Wzorek, M. (2021). Hastily formed knowledge networks and distributed situation awareness for collaborative robotics. Autonomous Intelligent Systems, 1(1), Article ID 16.
Open this publication in new window or tab >>Hastily formed knowledge networks and distributed situation awareness for collaborative robotics
2021 (English)In: Autonomous Intelligent Systems, E-ISSN 2730-616X, Vol. 1, no 1, article id 16Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Springer, 2021
Keywords
Multi-robot collaboration; Unmanned aerial vehicles; Distributed knowledge representation; Distributed situation awareness; Semantic web technology; Knowledge synchronization; Multi-agent human/robot interaction
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-199031 (URN)10.1007/s43684-021-00016-w (DOI)2-s2.0-85143187288 (Scopus ID)
Note

Funding Agencies|ELLIIT Network Organization for Information and Communication Technology, Sweden (Project B09) and the Swedish Foundation for Strategic Research SSF (Smart Systems Project RIT15-0097). The first author is also supported by an RExperts Program Grant 2020A1313030098 from the Guangdong Department of Science and Technology, China in addition to a Sichuan Province International Science and Technology Innovation Cooperation Project Grant 2020YFH0160.

Available from: 2023-11-07 Created: 2023-11-07 Last updated: 2023-11-16Bibliographically approved
Andersson, O., Wzorek, M. & Doherty, P. (2017). Deep Learning Quadcopter Control via Risk-Aware Active Learning. In: Satinder Singh and Shaul Markovitch (Ed.), Proceedings of The Thirty-first AAAI Conference on Artificial Intelligence (AAAI): . Paper presented at Thirty-First AAAI Conference on Artificial Intelligence (AAAI), 2017, San Francisco, February 4–9. (pp. 3812-3818). AAAI Press, 5
Open this publication in new window or tab >>Deep Learning Quadcopter Control via Risk-Aware Active Learning
2017 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
AAAI Press, 2017
Series
Proceedings of the AAAI Conference on Artificial Intelligence, ISSN 2159-5399, E-ISSN 2374-3468 ; 5
National Category
Computer Vision and Robotics (Autonomous Systems) Computer Sciences
Identifiers
urn:nbn:se:liu:diva-132800 (URN)000485630703119 ()978-1-57735-784-1 (ISBN)
Conference
Thirty-First AAAI Conference on Artificial Intelligence (AAAI), 2017, San Francisco, February 4–9.
Projects
ELLIITCADICSNFFP6SYMBICLOUDCUGS
Funder
Linnaeus research environment CADICSELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsEU, FP7, Seventh Framework ProgrammeCUGS (National Graduate School in Computer Science)Swedish Foundation for Strategic Research
Available from: 2016-11-25 Created: 2016-11-25 Last updated: 2023-04-05Bibliographically approved
Doherty, P., Kvarnström, J., Rudol, P., Wzorek, M., Conte, G., Berger, C., . . . Stastny, T. (2016). A Collaborative Framework for 3D Mapping using Unmanned Aerial Vehicles. In: Baldoni, M., Chopra, A.K., Son, T.C., Hirayama, K., Torroni, P. (Ed.), PRIMA 2016: Principles and Practice of Multi-Agent Systems: . Paper presented at PRIMA 2016: Principles and Practice of Multi-Agent Systems (pp. 110-130). Springer Publishing Company
Open this publication in new window or tab >>A Collaborative Framework for 3D Mapping using Unmanned Aerial Vehicles
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2016 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
Springer Publishing Company, 2016
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 9862
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:liu:diva-130558 (URN)10.1007/978-3-319-44832-9_7 (DOI)000388796200007 ()978-3-319-44831-2 (ISBN)
Conference
PRIMA 2016: Principles and Practice of Multi-Agent Systems
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsEU, FP7, Seventh Framework ProgrammeVINNOVASwedish Research Council
Note

Accepted for publication.

Available from: 2016-08-16 Created: 2016-08-16 Last updated: 2018-02-20
Berger, C., Rudol, P., Wzorek, M. & Kleiner, A. (2016). Evaluation of Reactive Obstacle Avoidance Algorithms for a Quadcopter. In: Proceedings of the 14th International Conference on Control, Automation, Robotics and Vision 2016 (ICARCV): . Paper presented at 14th International Conference on Control, Automation, Robotics and Vision (ICARCV), Phuket, Thailand, November 13-15, 2016. IEEE conference proceedings, Article ID Tu31.3.
Open this publication in new window or tab >>Evaluation of Reactive Obstacle Avoidance Algorithms for a Quadcopter
2016 (English)In: Proceedings of the 14th International Conference on Control, Automation, Robotics and Vision 2016 (ICARCV), IEEE conference proceedings, 2016, article id Tu31.3Conference paper, Published 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.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2016
Series
International Conference on Control Automation Robotics and Vision, ISSN 2474-2953
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-130956 (URN)10.1109/ICARCV.2016.7838803 (DOI)000405520900204 ()2-s2.0-85015170851 (Scopus ID)9781509035496 (ISBN)9781509047574 (ISBN)9781509035502 (ISBN)
Conference
14th International Conference on Control, Automation, Robotics and Vision (ICARCV), Phuket, Thailand, November 13-15, 2016
Note

Funding agencies:This work is partially supported by the Swedish Research Council (VR) Linnaeus Center CADICS, the ELLIIT network organization for Information and Communication Technology, and the Swedish Foundation for Strategic Research (CUAS Project, SymbiKCIoud Project).

Available from: 2016-09-01 Created: 2016-09-01 Last updated: 2018-01-10Bibliographically approved
Andersson, O., Wzorek, M., Rudol, P. & Doherty, P. (2016). Model-Predictive Control with Stochastic Collision Avoidance using Bayesian Policy Optimization. In: IEEE International Conference on Robotics and Automation (ICRA), 2016: . Paper presented at IEEE International Conference on Robotics and Automation (ICRA), 2016, Stockholm, May 16-21 (pp. 4597-4604). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Model-Predictive Control with Stochastic Collision Avoidance using Bayesian Policy Optimization
2016 (English)In: IEEE International Conference on Robotics and Automation (ICRA), 2016, Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 4597-4604Conference paper, Published 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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016
Series
Proceedings of IEEE International Conference on Robotics and Automation, ISSN 1050-4729
Keywords
Robot Learning, Collision Avoidance, Robotics, Bayesian Optimization, Model Predictive Control
National Category
Robotics Computer Sciences
Identifiers
urn:nbn:se:liu:diva-126769 (URN)10.1109/ICRA.2016.7487661 (DOI)000389516203138 ()
Conference
IEEE International Conference on Robotics and Automation (ICRA), 2016, Stockholm, May 16-21
Projects
CADICSELLIITNFFP6CUASSHERPA
Funder
Linnaeus research environment CADICSELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsEU, FP7, Seventh Framework ProgrammeSwedish Foundation for Strategic Research
Available from: 2016-04-04 Created: 2016-04-04 Last updated: 2023-04-05Bibliographically approved
Danelljan, M., Khan, F. S., Felsberg, M., Granström, K., Heintz, F., Rudol, P., . . . Doherty, P. (2015). A Low-Level Active Vision Framework for Collaborative Unmanned Aircraft Systems. In: Lourdes Agapito, Michael M. Bronstein and Carsten Rother (Ed.), Lourdes Agapito, Michael M. Bronstein and Carsten Rother (Ed.), COMPUTER VISION - ECCV 2014 WORKSHOPS, PT I: . Paper presented at 13th European Conference on Computer Vision (ECCV) Switzerland, September 6-7 and 12 (pp. 223-237). Springer Publishing Company, 8925
Open this publication in new window or tab >>A Low-Level Active Vision Framework for Collaborative Unmanned Aircraft Systems
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2015 (English)In: 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, Published 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

Place, publisher, year, edition, pages
Springer Publishing Company, 2015
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 8925
Keywords
Visual tracking; Visual surveillance; Micro UAV; Active vision
National Category
Computer Vision and Robotics (Autonomous Systems) Computer Sciences
Identifiers
urn:nbn:se:liu:diva-115847 (URN)10.1007/978-3-319-16178-5_15 (DOI)000362493800015 ()978-3-319-16177-8 (ISBN)978-3-319-16178-5 (ISBN)
Conference
13th European Conference on Computer Vision (ECCV) Switzerland, September 6-7 and 12
Available from: 2015-03-20 Created: 2015-03-20 Last updated: 2023-04-03Bibliographically approved
Doherty, P., Kvarnström, J., Wzorek, M., Rudol, P., Heintz, F. & Conte, G. (2014). HDRC3 - A Distributed Hybrid Deliberative/Reactive Architecture for Unmanned Aircraft Systems. In: Kimon P. Valavanis, George J. Vachtsevanos (Ed.), Handbook of Unmanned Aerial Vehicles: (pp. 849-952). Dordrecht: Springer Science+Business Media B.V.
Open this publication in new window or tab >>HDRC3 - A Distributed Hybrid Deliberative/Reactive Architecture for Unmanned Aircraft Systems
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2014 (English)In: 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.

Place, publisher, year, edition, pages
Dordrecht: Springer Science+Business Media B.V., 2014
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:liu:diva-113613 (URN)10.1007/978-90-481-9707-1_118 (DOI)978-90-481-9706-4 (ISBN)978-90-481-9707-1 (ISBN)
Funder
EU, FP7, Seventh Framework Programme, 600958Swedish Foundation for Strategic Research Linnaeus research environment CADICSeLLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Available from: 2015-01-26 Created: 2015-01-26 Last updated: 2018-01-11
Conte, G., Kleiner, A., Rudol, P., Korwel, K., Wzorek, M. & Doherty, P. (2013). Performance evaluation of a light weight multi-echo LIDAR for unmanned rotorcraft applications. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-1/W2: . Paper presented at Conference on Unmanned Aerial Vehicle in Geomatics (UAV-g 2013), 4-6 September 2013, Rostock, Germany. Copernicus Gesellschaft MBH
Open this publication in new window or tab >>Performance evaluation of a light weight multi-echo LIDAR for unmanned rotorcraft applications
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2013 (English)In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-1/W2, Copernicus Gesellschaft MBH , 2013Conference paper, Published 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.

Place, publisher, year, edition, pages
Copernicus Gesellschaft MBH, 2013
Series
International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 2194-9034
National Category
Robotics
Identifiers
urn:nbn:se:liu:diva-95889 (URN)000358305000016 ()
Conference
Conference on Unmanned Aerial Vehicle in Geomatics (UAV-g 2013), 4-6 September 2013, Rostock, Germany
Projects
Artificial Intelligence & Integrated Computer Systems
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsEU, FP7, Seventh Framework ProgrammeLinnaeus research environment CADICS
Available from: 2013-08-07 Created: 2013-08-07 Last updated: 2019-07-03
Wzorek, M. (2011). Selected Aspects of Navigation and Path Planning in Unmanned Aircraft Systems. (Licentiate dissertation). Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Selected Aspects of Navigation and Path Planning in Unmanned Aircraft Systems
2011 (English)Licentiate 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.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2011. p. 108
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1509
Keywords
Path planning, motion planning, autonomous Unmanned Aircraft Systems (UAS), Hierarchical Concurrent State Machines (HCSM), UAV
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-71147 (URN)LiU-Tek-Lic-2011:48 (Local ID)9789173930376 (ISBN)LiU-Tek-Lic-2011:48 (Archive number)LiU-Tek-Lic-2011:48 (OAI)
Presentation
2011-11-10, Alan Turing, Hus E, Campus Valla, Linköpings universitet, Linköping, 13:15 (English)
Opponent
Supervisors
Available from: 2011-11-28 Created: 2011-10-03 Last updated: 2020-08-21Bibliographically approved
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