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Choosing Path Replanning Strategies for Unmanned Aircraft Systems
Linköping University, Department of Computer and Information Science, UASTECH - Autonomous Unmanned Aircraft Systems Technologies. Linköping University, The Institute of Technology.
Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology. (Automated Planning and Diagnosis Group)ORCID iD: 0000-0002-5500-8494
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.
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. p. 193-200
Keywords [en]
artificial intelligence, path planning, motion planning, machine learning, autonomous unmanned vehicles, autonomous aircraft systems
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-59986ISBN: 978-1-57735-449-9 (print)OAI: oai:DiVA.org:liu-59986DiVA, id: diva2:354495
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 VRAvailable from: 2010-10-01 Created: 2010-10-01 Last updated: 2023-05-25
In thesis
1. Selected Functionalities for Autonomous Intelligent Systems in Public Safety Scenarios
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

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Wzorek, MariuszKvarnström, JonasDoherty, Patrick

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