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Mixed-Initiative Planning, Replanning and Execution: From Concept to Field Testing using AUV Fleets

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F17%3A10369866" target="_blank" >RIV/00216208:11320/17:10369866 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1109/IROS.2017.8206602" target="_blank" >http://dx.doi.org/10.1109/IROS.2017.8206602</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/IROS.2017.8206602" target="_blank" >10.1109/IROS.2017.8206602</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Mixed-Initiative Planning, Replanning and Execution: From Concept to Field Testing using AUV Fleets

  • Original language description

    Mission planning and execution for autonomous vehicles is crucial for their effective and efficient operation during scientific exploration, or search and rescue missions, to mention a few. Automated Planning has shown to be a useful tool for &apos;&apos;high level&apos;&apos; mission planning, that is, allocating tasks to vehicles while following given constraints (e.g., energy, collision avoidance). In this paper, we focus on making mission planning flexible and robust. That is, a human mission coordinator can modify tasks during the mission execution, so the tasks have to be dynamically reallocated during the process. Moreover, we assume that communication might not be reliable when vehicles are &apos;&apos;outside&apos;&apos;, i.e., performing the tasks, and thus we enforce vehicles to come back to their safe spots regularly. To address these requirements, we have developed two models, namely &apos;&apos;all tasks&apos;&apos; and &apos;&apos;one round&apos;&apos;, and integrated them to the control software. We have evaluated our approach in a field experiment focused on a mine-hunting scenario.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/GJ17-17125Y" target="_blank" >GJ17-17125Y: Balancing Deliberative and Reactive Behaviour of Intelligent Agents</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2017

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    IEEE International Conference on Robotics and Automation

  • ISBN

    978-1-5386-2681-8

  • ISSN

    2153-0858

  • e-ISSN

    neuvedeno

  • Number of pages

    6

  • Pages from-to

    6825-6830

  • Publisher name

    IEEE press

  • Place of publication

    New York, USA

  • Event location

    Vancouver, Kanada

  • Event date

    Sep 24, 2017

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article