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Paper Abstract and Keywords
Presentation 2026-05-14 13:50
Deep Reinforcement Learning-based Aerial Vehicle Path Planning for Low-Altitude Economy
Yuto Morioka, Shikhar Verma, Wei Ting Han, Mikifumi Shikida (KUT) IN2026-3
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, the Low Altitude Economy (LAE) has garnered significant attention, and path planning for aircraft requiring low-latency communication in high-density environments has become a critical challenge.However, conventional path planning algorithms do not sufficiently account for the characteristics of the LAE, such as the need for highly reliable, low-latency communication and support for multiple destinations, making them difficult to apply.In this study, we formulate the aircraft path planning problem in an LAE environment as an optimization problem that simultaneously satisfies travel time constraints and communication latency constraints, and propose a path planning method using PPO, a deep reinforcement learning algorithm. The proposed method adopts an observation structure that combines local grid information centered on the agent with a state vector, enabling scalable path planning that is independent of the environment’s size.Performance evaluation via simulation showed that the proposed method achieved a travel time constraint fulfillment rate up to 11.4 percentage points higher than that of a DQN-based method and a communication latency constraint fulfillment rate up to 8.6 percentage points higher than that of the A* algorithm.These results demonstrate that the proposed method is an effective approach to the aircraft path planning problem in LAE environments.
Keyword (in Japanese) (See Japanese page) 
(in English) Aerial vehicle path planning / Low-altitude economy / Deep reinforcement learning / Multi-objective optimization / / / /  
Reference Info. IEICE Tech. Rep., vol. 126, no. 22, IN2026-3, pp. 14-19, May 2026.
Paper # IN2026-3 
Date of Issue 2026-05-07 (IN) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF IN2026-3

Conference Information
Committee IN RCS NV  
Conference Date 2026-05-14 - 2026-05-15 
Place (in Japanese) (See Japanese page) 
Place (in English) Hiroshima City University Satellite Campus 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Ad-Hoc/Sensor Networks/MANET, Mobile Networks, M2M/IoT Communications, Wi-Fi, IEEE802.15(ZigBee) and others 
Paper Information
Registration To IN 
Conference Code 2026-05-IN-RCS-NV 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Deep Reinforcement Learning-based Aerial Vehicle Path Planning for Low-Altitude Economy 
Sub Title (in English)  
Keyword(1) Aerial vehicle path planning  
Keyword(2) Low-altitude economy  
Keyword(3) Deep reinforcement learning  
Keyword(4) Multi-objective optimization  
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1st Author's Name Yuto Morioka  
1st Author's Affiliation Kochi University of Technology (KUT)
2nd Author's Name Shikhar Verma  
2nd Author's Affiliation Kochi University of Technology (KUT)
3rd Author's Name Wei Ting Han  
3rd Author's Affiliation Kochi University of Technology (KUT)
4th Author's Name Mikifumi Shikida  
4th Author's Affiliation Kochi University of Technology (KUT)
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Speaker Author-1 
Date Time 2026-05-14 13:50:00 
Presentation Time 25 minutes 
Registration for IN 
Paper # IN2026-3 
Volume (vol) vol.126 
Number (no) no.22 
Page pp.14-19 
#Pages 6 
Date of Issue 2026-05-07 (IN) 

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