Adaptive UAV Swarm Mission Planning by Temporal Difference Learning

Shreevanth Krishnaa Gopalakrishnan; Saba Al-Rubaye; Gokhan Inalhan

doi:10.1109/DASC52595.2021.9594300

Adaptive UAV Swarm Mission Planning by Temporal Difference Learning

Shreevanth Krishnaa Gopalakrishnan, Saba Al-Rubaye, Gokhan Inalhan

Cranfield University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Citations (Scopus)

Abstract

The prevalence of Unmanned Aerial Vehicles (UAVs) in precision agriculture has been growing rapidly. This paper tackles the UAV global mission planning problem by incorporating a greater capacity for human-machine teaming in the architecture of a flexibly autonomous, near-fully-distributed Mission Management System for UAV swarms. Subsequently, the two problems of global mission planning are solved simultaneously using an integrated solution. This consists of a geometric clustering algorithm which prioritizes the minimization of overall mission time, and an off-policy, model-free Temporal Difference Learning global agent capable of learning about an initially unknown mission environment through simulations. The latter component makes the solution adaptive to missions with different requirements.

Original language	English
Title of host publication	40th Digital Avionics Systems Conference, DASC 2021 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665434201
DOIs	https://doi.org/10.1109/DASC52595.2021.9594300
Publication status	Published - 2021
Externally published	Yes
Event	40th IEEE/AIAA Digital Avionics Systems Conference, DASC 2021 - San Antonio, United States Duration: 3 Oct 2021 → 7 Oct 2021

Publication series

Name	AIAA/IEEE Digital Avionics Systems Conference - Proceedings
Volume	2021-October
ISSN (Print)	2155-7195
ISSN (Electronic)	2155-7209

Conference

Conference	40th IEEE/AIAA Digital Avionics Systems Conference, DASC 2021
Country/Territory	United States
City	San Antonio
Period	3/10/21 → 7/10/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Funding

ACKNOWLEDGMENT This research was supported by the grants received from Innovation UK under the program of Future Flight Challenge Phase 2: Strand 1, Development (Grant No #71017-Project Rise).

Funders	Funder number
Innovation UK

Keywords

Global Mission Planning
Precision Agriculture
Reinforcement Learning
Temporal Difference Learning
UAV

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/DASC52595.2021.9594300

Cite this

Gopalakrishnan, S. K., Al-Rubaye, S., & Inalhan, G. (2021). Adaptive UAV Swarm Mission Planning by Temporal Difference Learning. In 40th Digital Avionics Systems Conference, DASC 2021 - Proceedings (AIAA/IEEE Digital Avionics Systems Conference - Proceedings; Vol. 2021-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DASC52595.2021.9594300

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title = "Adaptive UAV Swarm Mission Planning by Temporal Difference Learning",

abstract = "The prevalence of Unmanned Aerial Vehicles (UAVs) in precision agriculture has been growing rapidly. This paper tackles the UAV global mission planning problem by incorporating a greater capacity for human-machine teaming in the architecture of a flexibly autonomous, near-fully-distributed Mission Management System for UAV swarms. Subsequently, the two problems of global mission planning are solved simultaneously using an integrated solution. This consists of a geometric clustering algorithm which prioritizes the minimization of overall mission time, and an off-policy, model-free Temporal Difference Learning global agent capable of learning about an initially unknown mission environment through simulations. The latter component makes the solution adaptive to missions with different requirements.",

keywords = "Global Mission Planning, Precision Agriculture, Reinforcement Learning, Temporal Difference Learning, UAV",

author = "Gopalakrishnan, \{Shreevanth Krishnaa\} and Saba Al-Rubaye and Gokhan Inalhan",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 40th IEEE/AIAA Digital Avionics Systems Conference, DASC 2021 ; Conference date: 03-10-2021 Through 07-10-2021",

year = "2021",

doi = "10.1109/DASC52595.2021.9594300",

language = "English",

series = "AIAA/IEEE Digital Avionics Systems Conference - Proceedings",

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Gopalakrishnan, SK, Al-Rubaye, S & Inalhan, G 2021, Adaptive UAV Swarm Mission Planning by Temporal Difference Learning. in 40th Digital Avionics Systems Conference, DASC 2021 - Proceedings. AIAA/IEEE Digital Avionics Systems Conference - Proceedings, vol. 2021-October, Institute of Electrical and Electronics Engineers Inc., 40th IEEE/AIAA Digital Avionics Systems Conference, DASC 2021, San Antonio, United States, 3/10/21. https://doi.org/10.1109/DASC52595.2021.9594300

Adaptive UAV Swarm Mission Planning by Temporal Difference Learning. / Gopalakrishnan, Shreevanth Krishnaa; Al-Rubaye, Saba; Inalhan, Gokhan.
40th Digital Avionics Systems Conference, DASC 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. (AIAA/IEEE Digital Avionics Systems Conference - Proceedings; Vol. 2021-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - The prevalence of Unmanned Aerial Vehicles (UAVs) in precision agriculture has been growing rapidly. This paper tackles the UAV global mission planning problem by incorporating a greater capacity for human-machine teaming in the architecture of a flexibly autonomous, near-fully-distributed Mission Management System for UAV swarms. Subsequently, the two problems of global mission planning are solved simultaneously using an integrated solution. This consists of a geometric clustering algorithm which prioritizes the minimization of overall mission time, and an off-policy, model-free Temporal Difference Learning global agent capable of learning about an initially unknown mission environment through simulations. The latter component makes the solution adaptive to missions with different requirements.

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Adaptive UAV Swarm Mission Planning by Temporal Difference Learning

Abstract

Publication series

Conference

Bibliographical note

Funding

Keywords

UN SDGs

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