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Drones
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Research of Intelligent Transportation Systems Using Unmanned Aerial Vehicles
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Research of Intelligent Transportation Systems Using Unmanned Aerial Vehicles

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Innovative Urban Mobility".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 7896

Special Issue Editors

Prof. Dr. Zhixiong Li

E-Mail Website
Guest Editor
Yonsei Frontier Lab, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
Interests: dynamic system modeling and optimization; sensor data analysis; machine learning and applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yong Ma

E-Mail Website1 Website2
Guest Editor
State Key Laboratory of Maritime Technology and Safety, School of Navigation, Wuhan University of Technology, 1178 Heping Road, Wuhan, Hubei, 430063, China
Interests: intelligent navigation for surface vehicles; path planning; path following; motion control; networked control algorithm
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, intelligent transportation systems (ITS) using unmanned aerial vehicles (UAV) are required. This Special Issue aims to emphasize the role of UAVs for autonomous systems in the ITS research field.

Artificial intelligence and machine learning enable modeling, controlling, and predicting the operations of UAVs; for example, connected UAVs, adaptive control of UAV swarm, and UAV path planning. All these emerging technologies can be applied to the ITS applications, including UAV-aided autonomous driving, UAV-based road environment perception, UAV-aided safety protection, etc. In order to perform effective analyses of new concepts and evaluate different design alternatives, while avoiding the risks and costs associated with extensive field experimentation, artificial intelligence and machine learning are crucial in ITS research using UAVs. Setting aside the epistemological significance of artificial intelligence and machine learning, the following question remains open: what is the exact contribution of artificial intelligence and machine learning, relative to UAV development, for intelligent transportation systems?

This Special Issue aims to provide a comprehensive overview of current ideas and findings in the modeling, controlling, and managing of UAVs in the ITS applications, including those in land, ocean, and aerial transportation. This Special Issue invites original and innovative research papers with critical perspectives on current and new approaches applied, and we encourage the submission of works investigating new theoretical methods to manually or automatically assess the success of models. The papers collected in this Special Issue will cover the following topics from diverse multi- and cross-disciplinary perspectives, including theoretical and numerical methods and experimental studies.

(I) To present the current state of the art of UAVs with regard to the design of experiments, field observations, and mathematical modeling

(II) To identify potential research directions and technologies that will drive innovations in the field of ITS using UAV.

Additionally, this Special Issue welcomes submissions employing new and emerging technologies and approaches, such as virtual and augmented reality, artificial intelligence, and digital twin modeling.

 

Prof. Dr. Zhixiong Li
Prof. Dr. Yong Ma
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. Drones 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 2600 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

  • intelligent transportation systems
  • unmanned aerial vehicles
  • artificial intelligence and machine learning
  • deep learning
  • reinforcement learning

Published Papers (3 papers)

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Research

28 pages, 1689 KiB  
Article
Cable-Driven Unmanned Aerial Manipulator Systems for Water Sampling: Design, Modeling, and Control
by Li Ding, Guibing Zhu, Yangmin Li and Yaoyao Wang
Drones 2023, 7(7), 450; https://doi.org/10.3390/drones7070450 - 06 Jul 2023
Viewed by 1268
Abstract
The unmanned aerial manipulator (UAM) is a kind of aerial robot that combines a quadrotor aircraft and an onboard manipulator. This paper focuses on the problems of structure design, system modeling, and motion control of an UAM applied for water sampling. A novel, [...] Read more.
The unmanned aerial manipulator (UAM) is a kind of aerial robot that combines a quadrotor aircraft and an onboard manipulator. This paper focuses on the problems of structure design, system modeling, and motion control of an UAM applied for water sampling. A novel, light, cable-driven UAM has been designed. The drive motors installed in the base transmit the force and motion remotely through cables, which can reduce the inertia ratio of the manipulator. The Newton–Euler method and Lagrangian method are adopted to establish the quadrotor model and manipulator model, respectively. External disturbances, model uncertainty, and joint flexibility are also accounted for in the two submodels. The quadrotor and manipulator are controlled separately to ensure the overall accurate aerial operation of the UAM. Specifically, a backstepping control method is designed with the disturbance observer (BC-DOB) technique for the position loop and attitude loop control of the quadrotor. A backstepping integral fast terminal sliding mode control based on the linear extended state observer (BIFTSMC-LESO) has been developed for the manipulator to provide precise manipulation. The DOB and LESO serve as compensators to estimate the external disturbances and model uncertainty. The Lyapunov theory is used to ensure the stability of the two controllers. Three simulation cases are conducted to test the superior performance of the proposed quadrotor controller and manipulator controller. All the results show that the proposed controllers provide better performances than other traditional controllers, which can complete the task of water quality sampling well. Full article
(This article belongs to the Special Issue Research of Intelligent Transportation Systems Using Unmanned Aerial Vehicles)
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16 pages, 1622 KiB  
Article
Leveraging UAVs to Enable Dynamic and Smart Aerial Infrastructure for ITS and Smart Cities: An Overview
by Michael C. Lucic, Omar Bouhamed, Hakim Ghazzai, Abdullah Khanfor and Yehia Massoud
Drones 2023, 7(2), 79; https://doi.org/10.3390/drones7020079 - 23 Jan 2023
Cited by 8 | Viewed by 2246
Abstract
Micro-unmanned aerial vehicles (UAVs), also known as drones, have been recognized as an emerging technology offering a plethora of applications touching various aspects of our lives, such as surveillance, agriculture, entertainment, and intelligent transportation systems (ITS). Furthermore, due to their low cost and [...] Read more.
Micro-unmanned aerial vehicles (UAVs), also known as drones, have been recognized as an emerging technology offering a plethora of applications touching various aspects of our lives, such as surveillance, agriculture, entertainment, and intelligent transportation systems (ITS). Furthermore, due to their low cost and ability to be fitted with transmitters, cameras, and other on-board sensors, UAVs can be seen as potential flying Internet-of-things (IoT) devices interconnecting with their environment and allowing for more mobile flexibility in the network. This paper overviews the beneficial applications that UAVs can offer to smart cities, and particularly to ITS, while highlighting the main challenges that can be encountered. Afterward, it proposes several potential solutions to organize the operation of UAV swarms, while addressing one of their main issues: their battery-limited capacity. Finally, open research areas that should be undertaken to strengthen the case for UAVs to become part of the smart infrastructure for futuristic cities are discussed. Full article
(This article belongs to the Special Issue Research of Intelligent Transportation Systems Using Unmanned Aerial Vehicles)
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14 pages, 3717 KiB  
Article
GGT-YOLO: A Novel Object Detection Algorithm for Drone-Based Maritime Cruising
by Yongshuai Li, Haiwen Yuan, Yanfeng Wang and Changshi Xiao
Drones 2022, 6(11), 335; https://doi.org/10.3390/drones6110335 - 31 Oct 2022
Cited by 17 | Viewed by 3412
Abstract
Drones play an important role in the development of remote sensing and intelligent surveillance. Due to limited onboard computational resources, drone-based object detection still faces challenges in actual applications. By studying the balance between detection accuracy and computational cost, we propose a novel [...] Read more.
Drones play an important role in the development of remote sensing and intelligent surveillance. Due to limited onboard computational resources, drone-based object detection still faces challenges in actual applications. By studying the balance between detection accuracy and computational cost, we propose a novel object detection algorithm for drone cruising in large-scale maritime scenarios. Transformer is introduced to enhance the feature extraction part and is beneficial to small or occluded object detection. Meanwhile, the computational cost of the algorithm is reduced by replacing the convolution operations with simpler linear transformations. To illustrate the performance of the algorithm, a specialized dataset composed of thousands of images collected by drones in maritime scenarios is given, and quantitative and comparative experiments are conducted. By comparison with other derivatives, the detection precision of the algorithm is increased by 1.4%, the recall is increased by 2.6% and the average precision is increased by 1.9%, while the parameters and floating-point operations are reduced by 11.6% and 7.3%, respectively. These improvements are thought to contribute to the application of drones in maritime and other remote sensing fields. Full article
(This article belongs to the Special Issue Research of Intelligent Transportation Systems Using Unmanned Aerial Vehicles)
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