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Machines
Special Issues
Flight Control and Path Planning of Unmanned Aerial Vehicles
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Flight Control and Path Planning of Unmanned Aerial Vehicles

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Automation and Control Systems".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1279

Special Issue Editors

Dr. Jonatan Alvarez Munoz

E-Mail Website
Guest Editor
Autonomous Aerial Systems Lab., IPSA, 63 boulevard de Brandebourg, 94200 Ivry-sur-Seine, France
Interests: drones; nonlinear control; UAV swarm; collaborative control; event-based control
Dr. Assia Belbachir

E-Mail Website
Guest Editor
NORCE Norwegian Research Centre, 4879 Grimstad, Norway
Interests: autonomous robots; cooperative systems; task planning; machine learning

Special Issue Information

Dear Colleagues,

Unmanned aerial vehicles (UAVs) have gained a lot of popularity in recent years. Because these systems appear to be flexible, have low cost and exhibit different levels of autonomy, they have been used in various applications. Examples of missions where UAVs are used include monitoring, inspection or surveillance, and, more recently, tasks where UAVs can interact with the environment, such as infrastructure inspection or logistics delivery.

Mission safety and efficiency are fundamental factors when implementing UAVs for such tasks. In this context, several research proposals and technologies are being continuously developed and improved by the scientific community, allowing promising results and expanding UAVs’ capabilities. Among the research challenges, autonomous path planning and flight control are essential issues for UAVs. The first one allows us to find an optimal path between a source and destination, satisfying constraints based on environmental information and mission requirements. The latter is required to ensure the optimal tracking of paths, as well as disturbance or fault tolerance. This Special Issue aims at collecting original research works related to path planning and flight control of UAVs.

Dr. Jonatan Alvarez Munoz
Dr. Assia Belbachir
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • unmanned aerial vehicles (UAVs)
  • path planning and assignment for UAVs
  • autonomous outdoor and indoor navigation
  • control algorithms
  • optimal path planning of UAV swarms
  • distributed control algorithms

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Published Papers (1 paper)

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Research

29 pages, 4506 KiB  
Article
Unmanned Aerial Vehicle Path Planning in Complex Dynamic Environments Based on Deep Reinforcement Learning
by Jiandong Liu, Wei Luo, Guoqing Zhang and Ruihao Li
Machines 2025, 13(2), 162; https://doi.org/10.3390/machines13020162 - 18 Feb 2025
Viewed by 934
Abstract
In this paper, an enhanced deep reinforcement learning approach is presented for unmanned aerial vehicles (UAVs) operating in dynamic and potentially hazardous environments. Initially, the capability to discern obstacles from visual data is achieved through the application of the Yolov8-StrongSort technique. Concurrently, a [...] Read more.
In this paper, an enhanced deep reinforcement learning approach is presented for unmanned aerial vehicles (UAVs) operating in dynamic and potentially hazardous environments. Initially, the capability to discern obstacles from visual data is achieved through the application of the Yolov8-StrongSort technique. Concurrently, a novel data storage system for deep Q-networks (DQN), named dynamic data memory (DDM), is introduced to hasten the learning process and convergence for UAVs. Furthermore, addressing the issue of UAVs’ paths veering too close to obstacles, a novel strategy employing an artificial potential field to adjust the reward function is introduced, which effectively guides the UAVs away from proximate obstacles. Rigorous simulation tests in an AirSim-based environment confirm the effectiveness of these methods. Compared to DQN, dueling DQN, M-DQN, improved Q-learning, DDM-DQN, EPF (enhanced potential field), APF-DQN, and L1-MBRL, our algorithm achieves the highest success rate of 77.67%, while also having the lowest average number of moving steps. Additionally, we conducted obstacle avoidance experiments with UAVs with different densities of obstacles. These tests highlight fast learning convergence and real-time obstacle detection and avoidance, ensuring successful achievement of the target. Full article
(This article belongs to the Special Issue Flight Control and Path Planning of Unmanned Aerial Vehicles)
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