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Applied Sciences
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Trends in Embodied-Intelligence Unmanned Vehicle Technology and Applications of Intelligent...
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Trends in Embodied-Intelligence Unmanned Vehicle Technology and Applications of Intelligent Transport Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: 20 August 2024 | Viewed by 2627

Special Issue Editors

Dr. Jian Chen

E-Mail Website
Guest Editor
College of Engineering, China Agricultural University, Beijing 100083, China
Interests: bio-inspired perception and embodied intelligent control of unmanned systems
Special Issues, Collections and Topics in MDPI journals
Dr. Qingchun Feng

E-Mail Website
Guest Editor
Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
Interests: agricultural robotics control; compliant operation; intelligence sensing technology; deep reinforcement learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are happy to announce a Special Issue entitled "Trends in Embodied Intelligence Unmanned Vehicle Technology and Applications of Intelligent Transport Systems". Applied Sciences is an international, peer-reviewed, open-access journal covering all aspects of applied nature sciences and published by MDPI semimonthly. This Special Issue will complete a group of international papers published in the field of vehicle technology and intelligent transport systems, with its rapid growth in popularity anticipated.

Currently, new embodied intelligence unmanned vehicle technology and intelligent transport systems are in an era of transformation. In the foreseeable future, unmanned vehicles represented by UGV (unmanned ground vehicles) and UAV (unmanned aerial vehicles) will be built using novel ground and air transportation, logistics, and operation systems; these will offer vast potential in various applicative fields of industry and agriculture. The integration of autonomous vehicle technology and aircraft technology is also breeding a new vehicle, namely the flying car, which is also known as a heavy-load vertical-takeoff and landing aircraft. Interactive perception, decision-making with a capacity for learning, and self-growth behavior are important features of embodied intelligence vehicles and intelligent transport systems, such as unmanned driving vehicles, intelligent agricultural machinery equipment, etc. Correspondingly, multi-sensor (LiDAR, millimeter wave radar, and optical sensors) and multi-source information fusion technology, SLAM technology, and bio-inspired visual technology are applied at the perception stage. Brain-inspired intelligence and end-to-end deep learning neural networks are applied to the decision-making stage. Disturbance self-rejection control, integrated control technology, bio-inspired formation control, and manned/unmanned hybrid cooperative control are applied to the behavior control stage.

We welcome manuscripts from all areas of vehicle technology and intelligent transport systems that may be of interest to international readers. To improve the quality and visibility of the journal, we encourage the submission of well-designed studies and high-quality datasets. Original research articles and comprehensive review papers are also welcome. The papers in this Special Issue will be published with full open access after peer review, for the benefit of both authors and readers.

Potential topics include, but are not limited to, the following:

  • Autonomous driving, intelligent driving, and unmanned driving;
  • Embodied intelligence;
  • Perception, cognition, and behavior;
  • SLAM(simultaneous localization and mapping);
  • LiDAR(light detection and ranging), millimeter wave radar, RGB and RGB-D cameras, and multi-spectral optical sensors;
  • Interactive perception;
  • Decision making with learning ability;
  • Self-growth control;
  • Bio-inspired visual perception;
  • Multi-sensor and multi-source information fusion;
  • Brain-inspired intelligence, and end-to-end deep learning neural network;
  • Disturbance observer, and disturbance self-rejection control;
  • Integration technology of perception, decision-making, and control;
  • Bio-inspired formation control;
  • Hybrid cooperative control of manned and unmanned vehicles.

We look forward to your contributions.

Dr. Jian Chen
Dr. Qingchun Feng
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. Applied Sciences is an international peer-reviewed open access semimonthly 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

  • vehicle
  • unmanned systems
  • embodied intelligence
  • agricultural and industrial applications
  • intelligent transport
  • autonomous driving
  • UGV
  • UAV
  • SLAM
  • perception
  • decision-making
  • control

Published Papers (3 papers)

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Research

18 pages, 11279 KiB  
Article
GNSS and LiDAR Integrated Navigation Method in Orchards with Intermittent GNSS Dropout
by Yilong Li, Qingchun Feng, Chao Ji, Jiahui Sun and Yu Sun
Appl. Sci. 2024, 14(8), 3231; https://0-doi-org.brum.beds.ac.uk/10.3390/app14083231 - 11 Apr 2024
Viewed by 569
Abstract
Considering the vulnerability of satellite positioning signals to obstruction and interference in orchard environments, this paper investigates a navigation and positioning method based on real-time kinematic global navigation satellite system (RTK-GNSS), inertial navigation system (INS), and light detection and ranging (LiDAR). This method [...] Read more.
Considering the vulnerability of satellite positioning signals to obstruction and interference in orchard environments, this paper investigates a navigation and positioning method based on real-time kinematic global navigation satellite system (RTK-GNSS), inertial navigation system (INS), and light detection and ranging (LiDAR). This method aims to enhance the research and application of autonomous operational equipment in orchards. Firstly, we design and integrate robot vehicles; secondly, we unify the positioning information of GNSS/INS and laser odometer through coordinate system transformation; next, we propose a dynamic switching strategy, whereby the system switches to LiDAR positioning when the GNSS signal is unavailable; and finally, we combine the kinematic model of the robot vehicles with PID and propose a path-tracking control system. The results of the orchard navigation experiment indicate that the maximum lateral deviation of the robotic vehicle during the path-tracking process was 0.35 m, with an average lateral error of 0.1 m. The positioning experiment under satellite signal obstruction shows that compared to the GNSS/INS integrated with adaptive Kalman filtering, the navigation system proposed in this article reduced the average positioning error by 1.6 m. Full article
(This article belongs to the Special Issue Trends in Embodied-Intelligence Unmanned Vehicle Technology and Applications of Intelligent Transport Systems)
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20 pages, 4393 KiB  
Article
A Framework for Communicating and Building a Digital Twin Model of the Electric Car
by Tomasz Bednarz, Andrzej Baier and Iwona Paprocka
Appl. Sci. 2024, 14(5), 1776; https://0-doi-org.brum.beds.ac.uk/10.3390/app14051776 - 22 Feb 2024
Cited by 1 | Viewed by 953
Abstract
The Fourth Industrial Revolution has had a huge impact on manufacturing processes and products. With rapidly growing technology, new solutions are being implemented in the field of digital representations of a physical product. This approach can provide benefits in terms of cost and [...] Read more.
The Fourth Industrial Revolution has had a huge impact on manufacturing processes and products. With rapidly growing technology, new solutions are being implemented in the field of digital representations of a physical product. This approach can provide benefits in terms of cost and testing time savings. In order to test and reflect the operation of an electric car, a digital twin model was designed. The paper collects all the information and standards necessary to transform the idea into a real and virtual model of an electric car. The significance and impact of the study on the improvement of the project are described. The research stand, correlations of components (DC and AC motors, shaft, and wheel of the electric car), and development prospects are presented in the paper. The communication method with the research stand is also presented. The digital twin should communicate in real time, which means obtaining the correct output when the input changes; the input is the AC motor current, and the output is the rotational speed of the DC motor. The relation between inputs and outputs are tested. The kinematics of the electric car are modelled in LabVIEW. The results obtained are compared with historic racing data. The track is also modeled based on satellite data, taking into account changes in terrain height, using the SG Telemetry Viewer application. The parameters of the electric car engine model are tuned based on actual data on the car’s speed and current in the electric motor. The achieved results are presented and then discussed. Full article
(This article belongs to the Special Issue Trends in Embodied-Intelligence Unmanned Vehicle Technology and Applications of Intelligent Transport Systems)
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20 pages, 5260 KiB  
Article
STEAM: Spatial Trajectory Enhanced Attention Mechanism for Abnormal UAV Trajectory Detection
by Namkyung Yoon, Dongjae Lee, Kiseok Kim, Taehoon Yoo, Hyeontae Joo and Hwangnam Kim
Appl. Sci. 2024, 14(1), 248; https://0-doi-org.brum.beds.ac.uk/10.3390/app14010248 - 27 Dec 2023
Viewed by 695
Abstract
Accurate unmanned aerial vehicle (UAV) trajectory tracking is crucial for the successful execution of UAV missions. Traditional global positioning system (GPS) methods face limitations in complex environments, and visual observation becomes challenging with distance and in low-light conditions. To address this challenge, we [...] Read more.
Accurate unmanned aerial vehicle (UAV) trajectory tracking is crucial for the successful execution of UAV missions. Traditional global positioning system (GPS) methods face limitations in complex environments, and visual observation becomes challenging with distance and in low-light conditions. To address this challenge, we propose a comprehensive framework for UAV trajectory verification, integrating a range-based ultra-wideband (UWB) positioning system and advanced image processing technologies. Our key contribution is the development of the Spatial Trajectory Enhanced Attention Mechanism (STEAM), a novel attention module specifically designed for analyzing and classifying UAV trajectory patterns. This system enables real-time UAV trajectory tracking and classification, facilitating swift and accurate assessment of adherence to predefined optimal trajectories. Another major contribution of our work is the integration of a UWB system for precise UAV location tracking, complemented by our advanced image processing approach that includes a deep neural network (DNN) for interpolating missing data from images, thereby significantly enhancing the model’s ability to detect abnormal maneuvers. Our experimental results demonstrate the effectiveness of the proposed framework in UAV trajectory tracking, showcasing its robust performance irrespective of raw data quality. Furthermore, we validate the framework’s performance using a lightweight learning model, emphasizing both its computational efficiency and exceptional classification accuracy. Full article
(This article belongs to the Special Issue Trends in Embodied-Intelligence Unmanned Vehicle Technology and Applications of Intelligent Transport Systems)
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