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Drones
Special Issues
Advanced Autonomous Mobility Toward Low-Altitude Economy and Three-Dimensional Transportation Systems
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Advanced Autonomous Mobility Toward Low-Altitude Economy and Three-Dimensional Transportation Systems

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

Deadline for manuscript submissions: 29 November 2024 | Viewed by 4470

Special Issue Editors

Dr. Xiangkun He

E-Mail Website
Guest Editor
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
Interests: autonomous mobility; reinforcement learning; trustworthy AI; embodied intelligence; robotic systems
Special Issues, Collections and Topics in MDPI journals
Dr. Hailong Huang

E-Mail Website
Guest Editor
Department of Aeronautical and Aviation Engineering, Hong Kong Polytechnic University, Hong Kong Special Administrative Region 999077, China
Interests: planning; navigation and control of unmanned aerial vehicles (UAVs); multi-agent systems; autonomous vehicles
Dr. Caizheng Wang

E-Mail Website
Guest Editor
Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 518110, China
Interests: electric vertical take-off and landing (eVTOL); intelligent unmanned systems
Dr. Henglai Wei

E-Mail Website
Guest Editor Assistant
School of Transportation Science and Engineering, Beihang University, Beijing 100083, China
Interests: intelligent unmanned system; decision and control; model predictive control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancement of autonomous vehicles, unmanned aerial vehicles (UAV), electric Vertical Take-off and Landing (eVTOL), advanced air mobility (AAM), and drone taxis, along with the increasing demand for efficient urban mobility solutions, are opening new frontiers in urban planning and logistics, promising to reshape our transportation systems as part of smart cities. This Special Issue aims to explore the cutting-edge developments in advanced autonomous mobility, with a particular focus on low-altitude economic activities and three-dimensional (3D) transportation systems.

We invite original research papers, comprehensive reviews, and visionary perspectives that address the technological challenges of this emerging field. Topics of interest include, but are not limited to:

  1. Autonomous aerial vehicles for urban air mobility;
  2. Autonomous ground vehicles for smart cities;
  3. Intelligent planning and control technologies for drones;
  4. End-to-end autonomous ground and aerial vehicle technologies;
  5. AI and machine learning applications in 3D traffic management;
  6. Cybersecurity and privacy concerns in connected aerial and ground mobility;
  7. 3D Transportation Infrastructure planning and design;
  8. Sensor technologies and perception systems for 3D transportation navigation;
  9. Energy-efficient propulsion systems for aerial vehicles;
  10. Human factors for aerial autonomous mobility.

This Special Issue aims to provide a comprehensive overview of the current state-of-the-art, challenges, and future directions in advanced autonomous mobility within the low-altitude economic sphere. We welcome contributions from academia, and industry to foster interdisciplinary dialogue and accelerate the development of safe, efficient, and sustainable 3D transportation systems.

Dr. Xiangkun He
Dr. Henglai Wei
Dr. Hailong Huang
Dr. Caizheng Wang
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

  • autonomous aerial vehicles for urban air mobility
  • autonomous ground vehicles for smart cities
  • end-to-end autonomous ground and aerial vehicle technologies
  • AI and machine learning applications in 3D traffic management
  • cybersecurity and privacy concerns in connected aerial and ground mobility 
  • autonomous multimodal mobility technologies
  • 3D transportation infrastructure planning and design
  • sensor technologies and perception systems for 3D transportation navigation
  • energy-efficient propulsion systems for aerial vehicles 
  • human factors for aerial autonomous mobility

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

12 pages, 3538 KiB  
Article
A Nonlinear Adaptive Control and Robustness Analysis for Autonomous Landing of UAVs
by Yue Feng, Quanwen Hu, Weihan Wu, Liaoni Wu, Qiuquan Guo and Haitao Zhang
Drones 2024, 8(10), 587; https://doi.org/10.3390/drones8100587 - 17 Oct 2024
Viewed by 577
Abstract
The UAV landing process has higher requirements for automatic flight control systems due to factors such as wind disturbances and strong constraints. Considering the proven effective adaptation of the out-of-loop L1 adaptive control (OLAC) system proposed in previous studies, this paper applies it [...] Read more.
The UAV landing process has higher requirements for automatic flight control systems due to factors such as wind disturbances and strong constraints. Considering the proven effective adaptation of the out-of-loop L1 adaptive control (OLAC) system proposed in previous studies, this paper applies it to landing control to enhance robustness and control accuracy in the presence of complex uncertainties. Based on modern control theory, an LQR-based OLAC algorithm for multi-input–multi-output (MIMO) systems is proposed, which is conducive to the coupling control of the flight attitude mode. To evaluate the robustness of the designed system, an equivalence stability margin analysis method for nonlinear systems is proposed based on parameter linearization. Along with a detailed autonomous landing strategy, including trajectory planning, control, and guidance, the effectiveness of the proposed methods is verified on a high-fidelity simulation platform. The Monte–Carlo simulation is implemented in the time domain, and the results demonstrate that OLAC exhibits strong robustness and ensures the state variables strictly meet the flight safety constraints. Full article
(This article belongs to the Special Issue Advanced Autonomous Mobility Toward Low-Altitude Economy and Three-Dimensional Transportation Systems)
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13 pages, 525 KiB  
Article
A Stochastic Drone-Scheduling Problem with Uncertain Energy Consumption
by Yandong He, Zhong Zheng, Huilin Li and Jie Deng
Drones 2024, 8(9), 430; https://doi.org/10.3390/drones8090430 - 26 Aug 2024
Viewed by 786
Abstract
In this paper, we present a stochastic drone-scheduling problem where the energy consumption of drones between any two nodes is uncertain. Considering uncertain energy consumption as opposed to deterministic energy consumption can effectively enhance the safety of drone flights. To address this issue, [...] Read more.
In this paper, we present a stochastic drone-scheduling problem where the energy consumption of drones between any two nodes is uncertain. Considering uncertain energy consumption as opposed to deterministic energy consumption can effectively enhance the safety of drone flights. To address this issue, we developed a two-stage stochastic programming model with recourse cost, and we employed a fixed-sample sampling strategy based on Monte Carlo simulation to characterize uncertain variables, followed by the design of an efficient variable neighborhood search algorithm to solve the model. Case study results indicate the superiority of our algorithm over genetic algorithms. Additionally, a comparison between deterministic and stochastic models suggests that considering the uncertainty in energy consumption can significantly enhance the average returns of unmanned aerial vehicle scheduling systems. Full article
(This article belongs to the Special Issue Advanced Autonomous Mobility Toward Low-Altitude Economy and Three-Dimensional Transportation Systems)
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20 pages, 9948 KiB  
Article
Traversability Analysis and Path Planning for Autonomous Wheeled Vehicles on Rigid Terrains
by Nan Wang, Xiang Li, Zhe Suo, Jiuchen Fan, Jixin Wang and Dongxuan Xie
Drones 2024, 8(9), 419; https://doi.org/10.3390/drones8090419 - 23 Aug 2024
Viewed by 767
Abstract
Autonomous vehicles play a crucial role in three-dimensional transportation systems and have been extensively investigated and implemented in mining and other fields. However, the diverse and intricate terrain characteristics present challenges to vehicle traversability, including complex geometric features such as slope, harsh physical [...] Read more.
Autonomous vehicles play a crucial role in three-dimensional transportation systems and have been extensively investigated and implemented in mining and other fields. However, the diverse and intricate terrain characteristics present challenges to vehicle traversability, including complex geometric features such as slope, harsh physical parameters such as friction and roughness, and irregular obstacles. The current research on traversability analysis primarily emphasizes the processing of perceptual information, with limited consideration for vehicle performance and state parameters, thereby restricting their applicability in path planning. A framework of traversability analysis and path planning methods for autonomous wheeled vehicles on rigid terrains is proposed in this paper for better traversability costs and less redundancy in path planning. The traversability boundary conditions are established first based on terrain and vehicle characteristics using theoretical methods to determine the traversable areas. Then, the traversability cost map for the traversable areas is obtained through simulation and segmented linear regression analysis. Afterward, the TV-Hybrid A* algorithm is proposed by redefining the path cost functions of the Hybrid A* algorithm through the simulation data and neural network method to generate a more cost-effective path. Finally, the path generated by the TV-Hybrid A* algorithm is validated and compared with that of the A* and Hybrid A* algorithms in simulations, demonstrating a slightly better traversability cost for the former. Full article
(This article belongs to the Special Issue Advanced Autonomous Mobility Toward Low-Altitude Economy and Three-Dimensional Transportation Systems)
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25 pages, 18894 KiB  
Article
Risk Assessment and Distribution Estimation for UAV Operations with Accurate Ground Feature Extraction Based on a Multi-Layer Method in Urban Areas
by Suyu Zhou, Yang Liu, Xuejun Zhang, Hailong Dong, Weizheng Zhang, Hua Wu and Hao Li
Drones 2024, 8(8), 399; https://doi.org/10.3390/drones8080399 - 15 Aug 2024
Viewed by 938
Abstract
In this paper, a quantitative ground risk assessment mechanism is proposed in which urban ground features are extracted based on high-resolution data in a satellite image when unmanned aerial vehicles (UAVs) operate in urban areas. Ground risk distributions are estimated and a risk [...] Read more.
In this paper, a quantitative ground risk assessment mechanism is proposed in which urban ground features are extracted based on high-resolution data in a satellite image when unmanned aerial vehicles (UAVs) operate in urban areas. Ground risk distributions are estimated and a risk map is constructed with a multi-layer method considering the comprehensive risk imposed by UAV operations. The urban ground feature extraction is first implemented by employing a K-Means clustering method to an actual satellite image. Five main categories of the ground features are classified, each of which is composed of several sub-categories. Three more layers are then obtained, which are a population density layer, a sheltering factor layer, and a ground obstacle layer. As a result, a three-dimensional (3D) risk map is formed with a high resolution of 1 m × 1 m × 5 m. For each unit in this risk map, three kinds of risk imposed by UAV operations are taken into account and calculated, which include the risk to pedestrians, risk to ground vehicles, and risk to ground properties. This paper also develops a method of the resolution conversion to accommodate different UAV operation requirements. Case study results indicate that the risk levels between the fifth and tenth layers of the generated 3D risk map are relatively low, making these altitudes quite suitable for UAV operations. Full article
(This article belongs to the Special Issue Advanced Autonomous Mobility Toward Low-Altitude Economy and Three-Dimensional Transportation Systems)
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