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A Framework for Safe Navigation of Unmanned Aerial Vehicles in Unknown Environments
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-3011-1505
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
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. p. 11-20
National Category
Robotics and automation
Identifiers
URN: urn:nbn:se:liu:diva-145815DOI: 10.1109/ICSEng.2017.58ISI: 000426505200002ISBN: 978-1-5386-0610-0 (print)OAI: oai:DiVA.org:liu-145815DiVA, id: diva2:1192173
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: 2025-02-09
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 graphics and computer vision 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: 2025-02-01Bibliographically approved

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