Imitation Learning-Based Unmanned Aerial Vehicle Planning for Multitarget Reconnaissance Under Uncertainty-Reference-Cited by-同舟云学术

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Imitation Learning-Based Unmanned Aerial Vehicle Planning for Multitarget Reconnaissance Under Uncertainty

Published:2020-01 Issue:1 Volume:17 Page:36-50
ISSN:2327-3097
Container-title:Journal of Aerospace Information Systems
language:en
Short-container-title:Journal of Aerospace Information Systems

Author:

Choi Uihwan¹,Ahn Jaemyung¹

Affiliation:

1. Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea

Funder

Agency for Defense Development

Publisher

American Institute of Aeronautics and Astronautics (AIAA)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Aerospace Engineering

Reference25 articles.

1. Toward a Fully Autonomous UAV: Research Platform for Indoor and Outdoor Urban Search and Rescue

2. Autonomous vehicle control systems — a review of decision making

3. Multidisciplinary Approach to Intelligent Unmanned-Airborne-Vehicles Mission Planning

4. Uncertain UAV ISR mission planning problem with multiple correlated objectives

5. Linear temporal logic vehicle routing with applications to multi-UAV mission planning

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1. Automated Aerial Docking System Using Onboard Vision-Based Deep Learning;Journal of Aerospace Information Systems;2022-06

2. Distributed Unmanned Aerial Vehicle Positioning for Multitarget Tracking with Bearing-Only Information;Journal of Aerospace Information Systems;2022-05

3. Risk assessment framework for low-altitude UAV traffic management;Journal of Intelligent & Fuzzy Systems;2022-02-02

4. Cooperative Trajectory Planning for Multi-target Tracking Using Passive Sensors;AIAA SCITECH 2022 Forum;2022-01-03

5. Trajectory design via unsupervised probabilistic learning on optimal manifolds;Data-Centric Engineering;2022

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