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H. Shiri, H. Seo, J. Park and M. Bennis, "Attention-Based Communication and Control for Multi-UAV Path Planning," in IEEE Wireless Communications Letters, vol. 11, no. 7, pp. 1409-1413, July 2022, doi: 10.1109/LWC.2022.3171602

Attention-based communication and control for multi-UAV path planning

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Author: Shiri, Hamid1; Seo, Hyowoon2; Park, Jihong3;
Organizations: 1Center for Wireless Communications, University of Oulu, 90014 Oulu, Finland
2Department of Electronics and Communications Engineering, Kwangwoon University, Seoul 01897, South Korea
3School of Information Technology, Deakin University, Geelong, VIC 3220, Australia
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 2.5 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2022100661265
Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2022-10-06
Description:

Abstract

Inspired by the multi-head attention (MHA) mechanism in natural language processing, this letter proposes an iterative single-head attention (ISHA) mechanism for multi-UAV path planning. The ISHA mechanism is run by a communication helper collecting the state embeddings of UAVs and distributing an attention score vector to each UAV. The attention scores computed by ISHA identify how many interactions with other UAVs should be considered in each UAV’s control decision-making. Simulation results corroborate that the ISHA-based communication and control framework achieves faster travel with lower inter-UAV collision risks than an MHA-aided baseline, particularly under limited communication resources.

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Series: IEEE wireless communications letters
ISSN: 2162-2337
ISSN-E: 2162-2345
ISSN-L: 2162-2337
Volume: 11
Issue: 7
Pages: 1409 - 1413
DOI: 10.1109/lwc.2022.3171602
OADOI: https://oadoi.org/10.1109/lwc.2022.3171602
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
UAV path planning
attention
communication-control co-design
multi-agent reinforcement learning
Article
Funding: This work was supported in part by the Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT under Grant 2021M1B3A3102358; in part by the Academy of Finland 6G Flagship under Grant 318927 and Project SMARTER; in part by the Projects EU-ICT IntellIoT under Grant 957218 and EUCHISTERA LearningEdge; and in part by the CONNECT, Infotech- NOOR, and NEGEIN
EU Grant Number: (957218) IntellIoT - Intelligent, distributed, human-centered and trustworthy IoT environments
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
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