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Multi-Unmanned Aerial Vehicle (Multi-UAV) for Autonomous Transportation

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Vehicular Sensing".

Deadline for manuscript submissions: closed (20 February 2024) | Viewed by 37370

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Special Issue Editors

Prof. Dr. Baochang Zhang

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Guest Editor

School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
Interests: robotic vision; path planning of UAVs; pattern recognition; machine learning; face recognition; wavelets
Special Issues, Collections and Topics in MDPI journals

Dr. Ying Huang

E-Mail Website
Guest Editor

Virtual Reality and Intelligent System, Hangzhou Normal University, Hangzhou 311121, China
Interests: computer vision; machine learning; artificial intelligence; control theory

Special Issue Information

Dear Colleagues,

Unmanned aerial vehicles (UAVs) are of particular interest today due to their varied, established and emerging applications, including in systems with multiple UAVs. However, as the application of several UAVs is a relatively new topic, there are several challenges that still need to be tackled, such as modeling, algorithms, coordination, planning, regulations, and simulations.

The objective of this Special Issue is to publish high-quality papers that address the challenging domain of multiple UAVs for autonomous transportation. We solicit original, full-length, unpublished contributions in this domain. Potential topics of interest include but are not limited to:

UAV configuration and grouping;
Spatial–temporal association;
Task separation;
Mission control;
Flight control;
Human–UAV interaction;
Autonomous planning;
Computer vision;
Navigation;
Edge AI.

Prof. Dr. Baochang Zhang
Dr. Ying Huang
Guest Editors

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Published Papers (10 papers)

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Research

18 pages, 31895 KiB

Open AccessArticle

TED-Face: Texture-Enhanced Deep Face Reconstruction in the Wild

by Ying Huang, Lin Fang and Shanfeng Hu

Sensors 2023, 23(14), 6525; https://doi.org/10.3390/s23146525 - 19 Jul 2023

Cited by 1 | Viewed by 2594

Abstract

We present TED-Face, a new method for recovering high-fidelity 3D facial geometry and appearance with enhanced textures from single-view images. While vision-based face reconstruction has received intensive research in the past decades due to its broad applications, it remains a challenging problem because human eyes are particularly sensitive to numerically minute yet perceptually significant details. Previous methods that seek to minimize reconstruction errors within a low-dimensional face space can suffer from this issue and generate close yet low-fidelity approximations. The loss of high-frequency texture details is a key factor in their process, which we propose to address by learning to recover both dense radiance residuals and sparse facial texture features from a single image, in addition to the variables solved by previous work—shape, appearance, illumination, and camera. We integrate the estimation of all these factors in a single unified deep neural network and train it on several popular face reconstruction datasets. We also introduce two new metrics, visual fidelity (VIF) and structural similarity (SSIM), to compensate for the fact that reconstruction error is not a consistent perceptual metric of quality. On the popular FaceWarehouse facial reconstruction benchmark, our proposed system achieves a VIF score of 0.4802 and an SSIM score of 0.9622, improving over the state-of-the-art Deep3D method by 6.69% and 0.86%, respectively. On the widely used LS3D-300W dataset, we obtain a VIF score of 0.3922 and an SSIM score of 0.9079 for indoor images, and the scores for outdoor images are 0.4100 and 0.9160, respectively, which also represent an improvement over those of Deep3D. These results show that our method is able to recover visually more realistic facial appearance details compared with previous methods. Full article

(This article belongs to the Special Issue Multi-Unmanned Aerial Vehicle (Multi-UAV) for Autonomous Transportation)

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16 pages, 3405 KiB

Open AccessArticle

An Optimized Convolutional Neural Network for the 3D Point-Cloud Compression

by Guoliang Luo, Bingqin He, Yanbo Xiong, Luqi Wang, Hui Wang, Zhiliang Zhu and Xiangren Shi

Sensors 2023, 23(4), 2250; https://doi.org/10.3390/s23042250 - 16 Feb 2023

Cited by 4 | Viewed by 3910

Abstract

Due to the tremendous volume taken by the 3D point-cloud models, knowing how to achieve the balance between a high compression ratio, a low distortion rate, and computing cost in point-cloud compression is a significant issue in the field of virtual reality (VR). Convolutional neural networks have been used in numerous point-cloud compression research approaches during the past few years in an effort to progress the research state. In this work, we have evaluated the effects of different network parameters, including neural network depth, stride, and activation function on point-cloud compression, resulting in an optimized convolutional neural network for compression. We first have analyzed earlier research on point-cloud compression based on convolutional neural networks before designing our own convolutional neural network. Then, we have modified our model parameters using the experimental data to further enhance the effect of point-cloud compression. Based on the experimental results, we have found that the neural network with the 4 layers and 2 strides parameter configuration using the Sigmoid activation function outperforms the default configuration by 208% in terms of the compression-distortion rate. The experimental results show that our findings are effective and universal and make a great contribution to the research of point-cloud compression using convolutional neural networks. Full article

(This article belongs to the Special Issue Multi-Unmanned Aerial Vehicle (Multi-UAV) for Autonomous Transportation)

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15 pages, 4783 KiB

Open AccessArticle

Geometry Sampling-Based Adaption to DCGAN for 3D Face Generation

by Guoliang Luo, Guoming Xiong, Xiaojun Huang, Xin Zhao, Yang Tong, Qiang Chen, Zhiliang Zhu, Haopeng Lei and Juncong Lin

Sensors 2023, 23(4), 1937; https://doi.org/10.3390/s23041937 - 9 Feb 2023

Cited by 4 | Viewed by 2751

Abstract

Despite progress in the past decades, 3D shape acquisition techniques are still a threshold for various 3D face-based applications and have therefore attracted extensive research. Moreover, advanced 2D data generation models based on deep networks may not be directly applicable to 3D objects because of the different dimensionality of 2D and 3D data. In this work, we propose two novel sampling methods to represent 3D faces as matrix-like structured data that can better fit deep networks, namely (1) a geometric sampling method for the structured representation of 3D faces based on the intersection of iso-geodesic curves and radial curves, and (2) a depth-like map sampling method using the average depth of grid cells on the front surface. The above sampling methods can bridge the gap between unstructured 3D face models and powerful deep networks for an unsupervised generative 3D face model. In particular, the above approaches can obtain the structured representation of 3D faces, which enables us to adapt the 3D faces to the Deep Convolution Generative Adversarial Network (DCGAN) for 3D face generation to obtain better 3D faces with different expressions. We demonstrated the effectiveness of our generative model by producing a large variety of 3D faces with different expressions using the two novel down-sampling methods mentioned above. Full article

(This article belongs to the Special Issue Multi-Unmanned Aerial Vehicle (Multi-UAV) for Autonomous Transportation)

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23 pages, 24086 KiB

Open AccessArticle

Multi-UAV Path Planning Algorithm Based on BINN-HHO

by Sen Li, Ran Zhang, Yuanming Ding, Xutong Qin, Yajun Han and Huiting Zhang

Sensors 2022, 22(24), 9786; https://doi.org/10.3390/s22249786 - 13 Dec 2022

Cited by 10 | Viewed by 3042

Abstract

Multi-UAV (multiple unmanned aerial vehicles) flying in three-dimensional (3D) mountain environments suffer from low stability, long-planned path, and low dynamic obstacle avoidance efficiency. Spurred by these constraints, this paper proposes a multi-UAV path planning algorithm that consists of a bioinspired neural network and improved Harris hawks optimization with a periodic energy decline regulation mechanism (BINN-HHO) to solve the multi-UAV path planning problem in a 3D space. Specifically, in the procession of global path planning, an energy cycle decline mechanism is introduced into HHO and embed it into the energy function, which balances the algorithm’s multi-round dynamic iteration between global exploration and local search. Additionally, when the onboard sensors detect a dynamic obstacle during the flight, the improved BINN algorithm conducts a local path replanning for dynamic obstacle avoidance. Once the dynamic obstacles in the sensor detection area disappear, the local path planning is completed, and the UAV returns to the trajectory determined by the global planning. The simulation results show that the proposed Harris hawks algorithm has apparent superiorities in path planning and dynamic obstacle avoidance efficiency compared with the basic Harris hawks optimization, particle swarm optimization (PSO), and the sparrow search algorithm (SSA). Full article

(This article belongs to the Special Issue Multi-Unmanned Aerial Vehicle (Multi-UAV) for Autonomous Transportation)

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23 pages, 11659 KiB

Open AccessArticle

Research on Aerial Autonomous Docking and Landing Technology of Dual Multi-Rotor UAV

by Liang Wang, Xiangqian Jiang, Di Wang, Lisheng Wang, Zhijun Tu and Jianliang Ai

Sensors 2022, 22(23), 9066; https://doi.org/10.3390/s22239066 - 22 Nov 2022

Cited by 7 | Viewed by 2721

Abstract

This paper studies the cooperative control of multiple unmanned aerial vehicles (UAVs) with sensors and autonomous flight capabilities. In this paper, an architecture is proposed that takes a small quadrotor as a mission UAV and a large six-rotor as a platform UAV to provide an aerial take-off and landing platform and transport carrier for the mission UAV. The design of a tracking controller for an autonomous docking and landing trajectory system is the focus of this research. To examine the system’s overall design, a dual-machine trajectory-tracking control simulation platform is created via MATLAB/Simulink. Then, an autonomous docking and landing trajectory-tracking controller based on radial basis function proportional–integral–derivative control is designed, which fulfills the trajectory-tracking control requirements of the autonomous docking and landing process by efficiently suppressing the external airflow disturbance according to the simulation results. A YOLOv3-based vision pilot system is designed to calibrate the rate of the aerial docking and landing position to eight frames per second. The feasibility of the multi-rotor aerial autonomous docking and landing technology is verified using prototype flight tests during the day and at night. It lays a technical foundation for UAV transportation, autonomous take-off, landing in the air, and collaborative networking. In addition, compared with the existing technologies, our research completes the closed loop of the technical process through modeling, algorithm design and testing, virtual simulation verification, prototype manufacturing, and flight test, which have better realizability. Full article

(This article belongs to the Special Issue Multi-Unmanned Aerial Vehicle (Multi-UAV) for Autonomous Transportation)

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14 pages, 4030 KiB

Open AccessArticle

An Efficient LiDAR Point Cloud Map Coding Scheme Based on Segmentation and Frame-Inserting Network

by Qiang Wang, Liuyang Jiang, Xuebin Sun, Jingbo Zhao, Zhaopeng Deng and Shizhong Yang

Sensors 2022, 22(14), 5108; https://doi.org/10.3390/s22145108 - 7 Jul 2022

Cited by 7 | Viewed by 3128

Abstract

In this article, we present an efficient coding scheme for LiDAR point cloud maps. As a point cloud map consists of numerous single scans spliced together, by recording the time stamp and quaternion matrix of each scan during map building, we cast the point cloud map compression into the point cloud sequence compression problem. The coding architecture includes two techniques: intra-coding and inter-coding. For intra-frames, a segmentation-based intra-prediction technique is developed. For inter-frames, an interpolation-based inter-frame coding network is explored to remove temporal redundancy by generating virtual point clouds based on the decoded frames. We only need to code the difference between the original LiDAR data and the intra/inter-predicted point cloud data. The point cloud map can be reconstructed according to the decoded point cloud sequence and quaternion matrices. Experiments on the KITTI dataset show that the proposed coding scheme can largely eliminate the temporal and spatial redundancies. The point cloud map can be encoded to 1/24 of its original size with 2 mm-level precision. Our algorithm also obtains better coding performance compared with the octree and Google Draco algorithms. Full article

(This article belongs to the Special Issue Multi-Unmanned Aerial Vehicle (Multi-UAV) for Autonomous Transportation)

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20 pages, 2372 KiB

Open AccessArticle

Colored Petri Net Modelling and Evaluation of Drone Inspection Methods for Distribution Networks

by Aleksandra Fedorova, Viktar Beliautsou and Armin Zimmermann

Sensors 2022, 22(9), 3418; https://doi.org/10.3390/s22093418 - 29 Apr 2022

Cited by 7 | Viewed by 2886

Abstract

The UAV industry is developing rapidly and drones are increasingly used for monitoring industrial facilities. When designing such systems, operating companies have to find a system configuration of multiple drones that is near-optimal in terms of cost while achieving the required monitoring quality. Stochastic influences such as failures and maintenance have to be taken into account. Model-based systems engineering supplies tools and methods to solve such problems. This paper presents a method to model and evaluate such UAV systems with coloured Petri nets. It supports a modular view on typical setup elements and different types of UAVs and is based on UAV application standards. The model can be easily adapted to the most popular flight tasks and allows for estimating the monitoring frequency and determining the most appropriate grouping and configuration of UAVs, monitoring schemes, air time and maintenance periods. An important advantage is the ability to consider drone maintenance processes. Thus, the methodology will be useful in the conceptual design phase of UAVs, in monitoring planning, and in the selection of UAVs for specific monitoring tasks. Full article

(This article belongs to the Special Issue Multi-Unmanned Aerial Vehicle (Multi-UAV) for Autonomous Transportation)

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18 pages, 3159 KiB

Open AccessArticle

Tiny Vehicle Detection for Mid-to-High Altitude UAV Images Based on Visual Attention and Spatial-Temporal Information

by Ruonan Yu, Hongguang Li, Yalong Jiang, Baochang Zhang and Yufeng Wang

Sensors 2022, 22(6), 2354; https://doi.org/10.3390/s22062354 - 18 Mar 2022

Cited by 14 | Viewed by 2930

Abstract

Mid-to-high altitude Unmanned Aerial Vehicle (UAV) imagery can provide important remote sensing information between satellite and low altitude platforms, and vehicle detection in mid-to-high altitude UAV images plays a crucial role in land monitoring and disaster relief. However, the high background complexity of images and limited pixels of objects challenge the performance of tiny vehicle detection. Traditional methods suffer from poor adaptation ability to complex backgrounds, while deep neural networks (DNNs) have inherent defects in feature extraction of tiny objects with finite pixels. To address the issue above, this paper puts forward a vehicle detection method combining the DNNs-based and traditional methods for mid-to-high altitude UAV images. We first employ the deep segmentation network to exploit the co-occurrence of the road and vehicles, then detect tiny vehicles based on visual attention mechanism with spatial-temporal constraint information. Experimental results show that the proposed method achieves effective detection of tiny vehicles in complex backgrounds. In addition, ablation experiments are performed to inspect the effectiveness of each component, and comparative experiments on tinier objects are carried out to prove the superior generalization performance of our method in detecting vehicles with a limited size of 5 × 5 pixels or less. Full article

(This article belongs to the Special Issue Multi-Unmanned Aerial Vehicle (Multi-UAV) for Autonomous Transportation)

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16 pages, 2371 KiB

Open AccessArticle

Lightweight Detection Network Based on Sub-Pixel Convolution and Objectness-Aware Structure for UAV Images

by Xuanye Li, Hongguang Li, Yalong Jiang and Meng Wang

Sensors 2021, 21(16), 5656; https://doi.org/10.3390/s21165656 - 22 Aug 2021

Cited by 2 | Viewed by 3689

Abstract

Unmanned Aerial Vehicles (UAVs) can serve as an ideal mobile platform in various situations. Real-time object detection with on-board apparatus provides drones with increased flexibility as well as a higher intelligence level. In order to achieve good detection results in UAV images with complex ground scenes, small object size and high object density, most of the previous work introduced models with higher computational burdens, making deployment on mobile platforms more difficult.This paper puts forward a lightweight object detection framework. Besides being anchor-free, the framework is based on a lightweight backbone and a simultaneous up-sampling and detection module to form a more efficient detection architecture. Meanwhile, we add an objectness branch to assist the multi-class center point prediction, which notably improves the detection accuracy and only takes up very little computing resources. The results of the experiment indicate that the computational cost of this paper is 92.78% lower than the CenterNet with ResNet18 backbone, and the mAP is 2.8 points higher on the Visdrone-2018-VID dataset. A frame rate of about 220 FPS is achieved. Additionally, we perform ablation experiments to check on the validity of each part, and the method we propose is compared with other representative lightweight object detection methods on UAV image datasets. Full article

(This article belongs to the Special Issue Multi-Unmanned Aerial Vehicle (Multi-UAV) for Autonomous Transportation)

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17 pages, 1287 KiB

Open AccessArticle

ViTT: Vision Transformer Tracker

by Xiaoning Zhu, Yannan Jia, Sun Jian, Lize Gu and Zhang Pu

Sensors 2021, 21(16), 5608; https://doi.org/10.3390/s21165608 - 20 Aug 2021

Cited by 14 | Viewed by 7886

Abstract

This paper presents a new model for multi-object tracking (MOT) with a transformer. MOT is a spatiotemporal correlation task among interest objects and one of the crucial technologies of multi-unmanned aerial vehicles (Multi-UAV). The transformer is a self-attentional codec architecture that has been successfully used in natural language processing and is emerging in computer vision. This study proposes the Vision Transformer Tracker (ViTT), which uses a transformer encoder as the backbone and takes images directly as input. Compared with convolution networks, it can model global context at every encoder layer from the beginning, which addresses the challenges of occlusion and complex scenarios. The model simultaneously outputs object locations and corresponding appearance embeddings in a shared network through multi-task learning. Our work demonstrates the superiority and effectiveness of transformer-based networks in complex computer vision tasks and paves the way for applying the pure transformer in MOT. We evaluated the proposed model on the MOT16 dataset, achieving 65.7% MOTA, and obtained a competitive result compared with other typical multi-object trackers. Full article

(This article belongs to the Special Issue Multi-Unmanned Aerial Vehicle (Multi-UAV) for Autonomous Transportation)

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