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Epistemic planning for multi-robot systems in communication-restricted environments

Many real-world robotic applications such as search and rescue, disaster relief, and inspection operations are often set in unstructured environments with a restricted or unreliable communication infrastructure. In such environments, a multi-robot system must either be deployed to i) remain constant...

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Detalles Bibliográficos
Autores principales: Bramblett, Lauren, Bezzo, Nicola
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10243298/
https://www.ncbi.nlm.nih.gov/pubmed/37287473
http://dx.doi.org/10.3389/frobt.2023.1149439
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author Bramblett, Lauren
Bezzo, Nicola
author_facet Bramblett, Lauren
Bezzo, Nicola
author_sort Bramblett, Lauren
collection PubMed
description Many real-world robotic applications such as search and rescue, disaster relief, and inspection operations are often set in unstructured environments with a restricted or unreliable communication infrastructure. In such environments, a multi-robot system must either be deployed to i) remain constantly connected, hence sacrificing operational efficiency or ii) allow disconnections considering when and how to regroup. In communication-restricted environments, we insist that the latter approach is desired to achieve a robust and predictable method for cooperative planning. One of the main challenges in achieving this goal is that optimal planning in partially unknown environments without communication requires an intractable sequence of possibilities. To solve this problem, we propose a novel epistemic planning approach for propagating beliefs about the system’s states during communication loss to ensure cooperative operations. Typically used for discrete multi-player games or natural language processing, epistemic planning is a powerful representation of reasoning through events, actions, and belief revisions, given new information. Most robot applications use traditional planning to interact with their immediate environment and only consider knowledge of their own state. By including an epistemic notion in planning, a robot may enact depth-of-reasoning about the system’s state, analyzing its beliefs about each robot in the system. In this method, a set of possible beliefs about other robots in the system are propagated using a Frontier-based planner to accomplish the coverage objective. As disconnections occur, each robot tracks beliefs about the system state and reasons about multiple objectives: i) coverage of the environment, ii) dissemination of new observations, and iii) possible information sharing from other robots. A task allocation optimization algorithm with gossip protocol is used in conjunction with the epistemic planning mechanism to locally optimize all three objectives, considering that in a partially unknown environment, the belief propagation may not be safe or possible to follow and that another robot may be attempting an information relay using the belief state. Results indicate that our framework performs better than the standard solution for communication restrictions and even shows similar performance to simulations with no communication limitations. Extensive experiments provide evidence of the framework’s performance in real-world scenarios.
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spelling pubmed-102432982023-06-07 Epistemic planning for multi-robot systems in communication-restricted environments Bramblett, Lauren Bezzo, Nicola Front Robot AI Robotics and AI Many real-world robotic applications such as search and rescue, disaster relief, and inspection operations are often set in unstructured environments with a restricted or unreliable communication infrastructure. In such environments, a multi-robot system must either be deployed to i) remain constantly connected, hence sacrificing operational efficiency or ii) allow disconnections considering when and how to regroup. In communication-restricted environments, we insist that the latter approach is desired to achieve a robust and predictable method for cooperative planning. One of the main challenges in achieving this goal is that optimal planning in partially unknown environments without communication requires an intractable sequence of possibilities. To solve this problem, we propose a novel epistemic planning approach for propagating beliefs about the system’s states during communication loss to ensure cooperative operations. Typically used for discrete multi-player games or natural language processing, epistemic planning is a powerful representation of reasoning through events, actions, and belief revisions, given new information. Most robot applications use traditional planning to interact with their immediate environment and only consider knowledge of their own state. By including an epistemic notion in planning, a robot may enact depth-of-reasoning about the system’s state, analyzing its beliefs about each robot in the system. In this method, a set of possible beliefs about other robots in the system are propagated using a Frontier-based planner to accomplish the coverage objective. As disconnections occur, each robot tracks beliefs about the system state and reasons about multiple objectives: i) coverage of the environment, ii) dissemination of new observations, and iii) possible information sharing from other robots. A task allocation optimization algorithm with gossip protocol is used in conjunction with the epistemic planning mechanism to locally optimize all three objectives, considering that in a partially unknown environment, the belief propagation may not be safe or possible to follow and that another robot may be attempting an information relay using the belief state. Results indicate that our framework performs better than the standard solution for communication restrictions and even shows similar performance to simulations with no communication limitations. Extensive experiments provide evidence of the framework’s performance in real-world scenarios. Frontiers Media S.A. 2023-05-23 /pmc/articles/PMC10243298/ /pubmed/37287473 http://dx.doi.org/10.3389/frobt.2023.1149439 Text en Copyright © 2023 Bramblett and Bezzo. 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 Robotics and AI
Bramblett, Lauren
Bezzo, Nicola
Epistemic planning for multi-robot systems in communication-restricted environments
title Epistemic planning for multi-robot systems in communication-restricted environments
title_full Epistemic planning for multi-robot systems in communication-restricted environments
title_fullStr Epistemic planning for multi-robot systems in communication-restricted environments
title_full_unstemmed Epistemic planning for multi-robot systems in communication-restricted environments
title_short Epistemic planning for multi-robot systems in communication-restricted environments
title_sort epistemic planning for multi-robot systems in communication-restricted environments
topic Robotics and AI
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10243298/
https://www.ncbi.nlm.nih.gov/pubmed/37287473
http://dx.doi.org/10.3389/frobt.2023.1149439
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