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Behaviour Recognition with Kinodynamic Planning Over Continuous Domains

We investigate the application of state-of-the-art goal recognition techniques for behaviour recognition over complex continuous domains using model predictive control (MPC) for trajectory generation. We formally define the problem of kinodynamic behaviour recognition and establish a set of baseline...

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Autores principales: Fitzpatrick, Grady, Lipovetzky, Nir, Papasimeon, Michael, Ramirez, Miquel, Vered, Mor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8573250/
https://www.ncbi.nlm.nih.gov/pubmed/34761213
http://dx.doi.org/10.3389/frai.2021.717003
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author Fitzpatrick, Grady
Lipovetzky, Nir
Papasimeon, Michael
Ramirez, Miquel
Vered, Mor
author_facet Fitzpatrick, Grady
Lipovetzky, Nir
Papasimeon, Michael
Ramirez, Miquel
Vered, Mor
author_sort Fitzpatrick, Grady
collection PubMed
description We investigate the application of state-of-the-art goal recognition techniques for behaviour recognition over complex continuous domains using model predictive control (MPC) for trajectory generation. We formally define the problem of kinodynamic behaviour recognition and establish a set of baseline behaviours and performance measures in the complex domain of unmanned aerial maneuvers. We evaluate how well our approach performs over a range of standard aerial maneuvers and representative initial configurations of varying complexity. The work also highlights future research directions in compound model-based behaviour recognition and team behaviour recognition where multiple agents may be acting simultaneously.
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spelling pubmed-85732502021-11-09 Behaviour Recognition with Kinodynamic Planning Over Continuous Domains Fitzpatrick, Grady Lipovetzky, Nir Papasimeon, Michael Ramirez, Miquel Vered, Mor Front Artif Intell Artificial Intelligence We investigate the application of state-of-the-art goal recognition techniques for behaviour recognition over complex continuous domains using model predictive control (MPC) for trajectory generation. We formally define the problem of kinodynamic behaviour recognition and establish a set of baseline behaviours and performance measures in the complex domain of unmanned aerial maneuvers. We evaluate how well our approach performs over a range of standard aerial maneuvers and representative initial configurations of varying complexity. The work also highlights future research directions in compound model-based behaviour recognition and team behaviour recognition where multiple agents may be acting simultaneously. Frontiers Media S.A. 2021-10-25 /pmc/articles/PMC8573250/ /pubmed/34761213 http://dx.doi.org/10.3389/frai.2021.717003 Text en Copyright © 2021 Fitzpatrick, Lipovetzky, Papasimeon, Ramirez and Vered. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Artificial Intelligence
Fitzpatrick, Grady
Lipovetzky, Nir
Papasimeon, Michael
Ramirez, Miquel
Vered, Mor
Behaviour Recognition with Kinodynamic Planning Over Continuous Domains
title Behaviour Recognition with Kinodynamic Planning Over Continuous Domains
title_full Behaviour Recognition with Kinodynamic Planning Over Continuous Domains
title_fullStr Behaviour Recognition with Kinodynamic Planning Over Continuous Domains
title_full_unstemmed Behaviour Recognition with Kinodynamic Planning Over Continuous Domains
title_short Behaviour Recognition with Kinodynamic Planning Over Continuous Domains
title_sort behaviour recognition with kinodynamic planning over continuous domains
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8573250/
https://www.ncbi.nlm.nih.gov/pubmed/34761213
http://dx.doi.org/10.3389/frai.2021.717003
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