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Perception, Navigation, and Control for Unmanned Aerial Vehicles

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (30 August 2022) | Viewed by 24533

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Special Issue Editors

Dr. Jae-Mo Kang

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Guest Editor

Department of Artificial Intelligence, Kyungbook National University, Daegu 41566, Republic of Korea
Interests: unmanned aerial vehicles; AI-inspired perception, navigation, and control; signal-processing-based perception, navigation, and control; autonomous driving and navigation; recognition and perception techniques for unmanned aerial vehicles; velocity, energy, and trajectory controls for unmanned aerial vehicles
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Dr. Dong-Woo Lim

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Guest Editor

Radio and Satellite Research Division, Electronics and Telecommunications Research Institute, Daejeon 32800, Korea
Interests: Internet-of-Things-based UAV; deep reinforcement learning

Special Issue Information

Dear Colleagues,

Unmanned aerial vehicles, also known as drones, have drawn an upsurge of research interest and practical usage today as they can be utilized in many real-world scenarios and applications very effectively and efficiently. To realize such benefits in practice, however, it is very important to adaptively perceive, autonoously navigate, and smartly/intelligently control unmanned aerial vehicles according to the environments and other conditions.

The aim of this Special Issue is to disseminate the latest research as well as innovative techniques on perception, navigation, and control for unmanned aerial vehicles in various fields and applications. Review papers on this topic are also welcome. Potential topics include, but are not limited to artificial-intelligence-, machine/deep/reinforcement learning-, optimization-, and signal-processing-based perception, navigation, and control technologies for unmanned aerial vehicles. Papers on other-related theory, analytical/experimental approaches, developments, flatforms, and techniques are also welcome.

Dr. Jae-Mo Kang
Dr. Dong-Woo Lim
Guest Editor

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Published Papers (11 papers)

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Research

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22 pages, 12465 KiB

Open AccessArticle

Ground Target Detection and Damage Assessment by Patrol Missiles Based on YOLO-VGGNet

by Yibo Xu, Qinghua Yu, Yanjuan Wang, Junhao Xiao, Zhiqian Zhou and Huimin Lu

Appl. Sci. 2022, 12(19), 9484; https://doi.org/10.3390/app12199484 - 21 Sep 2022

Viewed by 1673

Abstract

Patrol missiles are a common type of unmanned aerial vehicle, which can be efficiently used for reconnaissance and sensing. In this work, target detection and the damage assessment of typical mobile ground targets by patrol missiles are studied, and a new method, combining the YOLO v3 with the VGG networks, is proposed for the problem. Specifically, with YOLO v3 as the precursor, the proposed method can detect, classify, and localize ground targets accurately and quickly. Then, the image blocks of detected targets are fed into the lightweight VGG networks, which can evaluate their damage level coarsely. Meanwhile, based on class activation mapping (CAM) and deconvolution, we further analyse the activation intensity of clustered convolution kernels, which helps determine whether the targets’ key components are destroyed. Unlike traditional image change detection methods, which require images before and after a strike for comparison, the proposed method learns the target model through extensive training and can assess the target’s damage status in a timely and online manner. Compared to previous learning-based methods, our detailed analysis with convolutional feature visualization of the damaged targets and their components gives a more interpretable perspective. Finally, Unity simulation experiments prove the proposed method’s effectiveness, which improves the accuracy of damage level assessment by 16.0% and 8.8% compared with traditional image-change-detection-based methods and the two-CNN learning-based method. The convolutional feature clustering method evaluates the status of the targets’ key components with an accuracy of 72%. Full article

(This article belongs to the Special Issue Perception, Navigation, and Control for Unmanned Aerial Vehicles)

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17 pages, 1427 KiB

Open AccessArticle

On the Communication Performance of Airborne Distributed Coherent Radar

by Qinhao Wu, Bo Zhang, Hongqiang Wang and Jinlin Peng

Appl. Sci. 2022, 12(13), 6351; https://doi.org/10.3390/app12136351 - 22 Jun 2022

Viewed by 1135

Abstract

Compared with a monostatic radar, airborne distributed coherent radar (ADCR) has been widely applied thanks to its flexibility, greater degree of freedom, stronger detection, and anti-jamming ability. Unlike distributed ground-based radar, the precondition for ADCR to perform tasks is maintenance of stable wireless communication links among the unmanned aerial vehicles (UAVs). Therefore, the communication channel modeling of ADCR is very important. This paper mainly analyzes the performance of a communication system composed of radar UAVs, communication UAV (relay), and ground base station. The probability density function (PDF) and outage probability (OP) of signal-to-noise ratio (SNR) at the ground terminal are derived analytically in the cases of transmission power error, UAV position error, and multi-path fading. Numerical simulation shows the validity of the derived results. Full article

(This article belongs to the Special Issue Perception, Navigation, and Control for Unmanned Aerial Vehicles)

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14 pages, 2962 KiB

Open AccessArticle

Deep Learning-Aided Downlink Beamforming Design and Uplink Power Allocation for UAV Wireless Communications with LoRa

by Yeong-Rok Kim, Jun-Hyun Park, Jae-Mo Kang, Dong-Woo Lim and Kyu-Min Kang

Appl. Sci. 2022, 12(10), 4826; https://doi.org/10.3390/app12104826 - 10 May 2022

Cited by 1 | Viewed by 1935

Abstract

In this paper, we consider an unmanned aerial vehicle (UAV) wireless communication system where a base station (BS) equipped multi antennas communicates with multiple UAVs, each equipped with a single antenna, using the LoRa (Long Range) modulation. The traditional approaches for downlink beamforming design or uplink power allocation rely on the convex optimization technique, which is prohibitive in practice or even infeasible for the UAVs with limited computing capabilities, because the corresponding convex optimization problems (such as second-order cone programming (SOCP) and linear programming (LP)) requiring a non-negligible complexity need to be re-solved many times while the UAVs move. To address this issue, we propose novel schemes for beamforming design for downlink transmission from the BS to the UAVs and power allocation for uplink transmission from the UAVs to the BS, respectively, based on deep learning. Numerical results demonstrate a constructed deep neural network (DNN) can predict the optimal value of the downlink beamforming or the uplink power allocation with low complexity and high accuracy. Full article

(This article belongs to the Special Issue Perception, Navigation, and Control for Unmanned Aerial Vehicles)

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10 pages, 916 KiB

Open AccessArticle

Joint Semantic Understanding with a Multilevel Branch for Driving Perception

by Dong-Gyu Lee and Yoon-Ki Kim

Appl. Sci. 2022, 12(6), 2877; https://doi.org/10.3390/app12062877 - 11 Mar 2022

Cited by 8 | Viewed by 2211

Abstract

Visual perception is a critical task for autonomous driving. Understanding the driving environment in real time can assist a vehicle in driving safely. In this study, we proposed a multi-task learning framework for simultaneous traffic object detection, drivable area segmentation, and lane line segmentation in an efficient way. Our network encoder extracts features from an input image and three decoders at multilevel branches handle specific tasks. The decoders share the feature maps with more similar tasks for joint semantic understanding. Multiple loss functions are automatically weighted summed to learn multiple objectives simultaneously. We demonstrate the effectiveness of this framework on a BerkeleyDeepDrive100K (BDD100K) dataset. In the experiment, the proposed method outperforms the competing multi-task and single-task methods in terms of accuracy and maintains a real-time inference at more than 37 frames per second. Full article

(This article belongs to the Special Issue Perception, Navigation, and Control for Unmanned Aerial Vehicles)

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18 pages, 1046 KiB

Open AccessArticle

Competing Miners: A Synergetic Solution for Combining Blockchain and Edge Computing in Unmanned Aerial Vehicle Networks

by Jacob Mathias Nilsen, Jun-Hyun Park, Sangseok Yun, Jae-Mo Kang and Heechul Jung

Appl. Sci. 2022, 12(5), 2581; https://doi.org/10.3390/app12052581 - 2 Mar 2022

Cited by 3 | Viewed by 1557

Abstract

Edge computing (EC) is very useful and particularly promising for many practical unmanned aerial vehicle (UAV) applications. Integrating the blockchain to this technology strengthens privacy protection and data integrity and also prevents data from being easily leaked. However, the required operations in the blockchain are computationally heavy because a blockchain requires devices to solve a complicated proof-of-work (PoW) puzzle to add new data (i.e., a block) to the blockchain. Solving a PoW requires substantial amounts of time and energy, which are big concerns for UAVs. In this article, we suggest a synergetic solution to address this issue based on multiple competing miners in a blockchain. Specifically, we present two novel frameworks for combining the blockchain and EC to effectively overcome several critical limitations when applying the blockchain to UAV and EC tasks, respectively. The goal of both of these proposed frameworks is to reduce both the time spent on mining and the energy consumption for the EC. We first look at the fundamentals of the blockchain with competing miners. Then, our proposed frameworks are described with experimental results, through which important insights are drawn. We finally discuss application scenarios for our proposed frameworks, the related technical challenges, and future research directions. Full article

(This article belongs to the Special Issue Perception, Navigation, and Control for Unmanned Aerial Vehicles)

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16 pages, 8388 KiB

Open AccessArticle

Mixer U-Net: An Improved Automatic Road Extraction from UAV Imagery

by Furkat Sultonov, Jun-Hyun Park, Sangseok Yun, Dong-Woo Lim and Jae-Mo Kang

Appl. Sci. 2022, 12(4), 1953; https://doi.org/10.3390/app12041953 - 13 Feb 2022

Cited by 12 | Viewed by 2765

Abstract

Automatic road extraction from unmanned aerial vehicle (UAV) imagery has been one of the major research topics in the area of remote sensing analysis due to its importance in a wide range of applications such as urban planning, road monitoring, intelligent transportation systems, and automatic road navigation. Thanks to the recent advances in Deep Learning (DL), the tedious manual segmentation of roads can be automated. However, the majority of these models are computationally heavy and, thus, are not suitable for UAV remote-sensing tasks with limited resources. To alleviate this bottleneck, we propose two lightweight models based on depthwise separable convolutions and ConvMixer inception block. Both models take the advantage of computational efficiency of depthwise separable convolutions and multi-scale processing of inception module and combine them in an encoder–decoder architecture of U-Net. Specifically, we substitute standard convolution layers used in U-Net for ConvMixer layers. Furthermore, in order to learn images on different scales, we apply ConvMixer layer into Inception module. Finally, we incorporate pathway networks along the skip connections to minimize the semantic gap between encoder and decoder. In order to validate the performance and effectiveness of the models, we adopt Massachusetts roads dataset. One incarnation of our models is able to beat the U-Net’s performance with 10× fewer parameters, and DeepLabV3’s performance with 12× fewer parameters in terms of mean intersection over union (mIoU) metric. For further validation, we have compared our models against four baselines in total and used additional metrics such as precision (P), recall (R), and F1 score. Full article

(This article belongs to the Special Issue Perception, Navigation, and Control for Unmanned Aerial Vehicles)

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12 pages, 926 KiB

Open AccessArticle

Hierarchical Federated Learning for Edge-Aided Unmanned Aerial Vehicle Networks

by Jamshid Tursunboev, Yong-Sung Kang, Sung-Bum Huh, Dong-Woo Lim, Jae-Mo Kang and Heechul Jung

Appl. Sci. 2022, 12(2), 670; https://doi.org/10.3390/app12020670 - 11 Jan 2022

Cited by 17 | Viewed by 3033

Abstract

Federated learning (FL) allows UAVs to collaboratively train a globally shared machine learning model while locally preserving their private data. Recently, the FL in edge-aided unmanned aerial vehicle (UAV) networks has drawn an upsurge of research interest due to a bursting increase in heterogeneous data acquired by UAVs and the need to build the global model with privacy; however, a critical issue is how to deal with the non-independent and identically distributed (non-i.i.d.) nature of heterogeneous data while ensuring the convergence of learning. To effectively address this challenging issue, this paper proposes a novel and high-performing FL scheme, namely, the hierarchical FL algorithm, for the edge-aided UAV network, which exploits the edge servers located in base stations as intermediate aggregators with employing commonly shared data. Experiment results demonstrate that the proposed hierarchical FL algorithm outperforms several baseline FL algorithms and exhibits better convergence behavior. Full article

(This article belongs to the Special Issue Perception, Navigation, and Control for Unmanned Aerial Vehicles)

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9 pages, 2353 KiB

Open AccessArticle

UAVC: A New Method for Correcting Lidar Overlap Factors Based on Unmanned Aerial Vehicle Vertical Detection

by Ming Zhao, Zhiyuan Fang, Hao Yang, Liangliang Cheng, Jianfeng Chen and Chenbo Xie

Appl. Sci. 2022, 12(1), 184; https://doi.org/10.3390/app12010184 - 24 Dec 2021

Cited by 4 | Viewed by 2197

Abstract

A method to calibrate the overlap factor of Lidar is proposed, named unmanned aerial vehicle correction (UAVC), which uses unmanned aerial vehicles (UAVs) to detect the vertical distribution of particle concentrations. The conversion relationship between the particulate matter concentration and the aerosol extinction coefficient is inverted by the high-altitude coincidence of the vertical detection profiles of the UAV and Lidar. Using this conversion relationship, the Lidar signal without the influence of the overlap factor can be inverted. Then, the overlap factor profile is obtained by comparing the signal with the original Lidar signal. A 355 nm Raman-Mie Lidar and UAV were used to measure overlap factors under different weather conditions. After comparison with the Raman method, it is found that the overlap factors calculated by the two methods are in good agreement. The changing trend of the extinction coefficient at each height is relatively consistent, after comparing the inversion result of the corrected Lidar signal with the ground data. The results show that after the continuously measured Lidar signal is corrected by the overlap factor measured by this method, low-altitude aerosol information can be effectively obtained. Full article

(This article belongs to the Special Issue Perception, Navigation, and Control for Unmanned Aerial Vehicles)

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24 pages, 3957 KiB

Open AccessArticle

Building a Realistic Virtual Simulator for Unmanned Aerial Vehicle Teleoperation

by Manuel Eduardo Mora-Soto, Javier Maldonado-Romo, Alejandro Rodríguez-Molina and Mario Aldape-Pérez

Appl. Sci. 2021, 11(24), 12018; https://doi.org/10.3390/app112412018 - 17 Dec 2021

Cited by 3 | Viewed by 2842

Abstract

Unmanned Aerial Vehicles (UAVs) support humans in performing an increasingly varied number of tasks. UAVs need to be remotely operated by a human pilot in many cases. Therefore, pilots require repetitive training to master the UAV movements. Nevertheless, training with an actual UAV involves high costs and risks. Fortunately, simulators are alternatives to face these difficulties. However, existing simulators lack realism, do not present flight information intuitively, and sometimes do not allow natural interaction with the human operator. This work addresses these issues through a framework for building realistic virtual simulators for the human operation of UAVs. First, the UAV is modeled in detail to perform a dynamic simulation in this framework. Then, the information of the above simulation is utilized to manipulate the elements in a virtual 3D operation environment developed in Unity 3D. Therefore, the interaction with the human operator is introduced with a proposed teleoperation algorithm and an input device. Finally, a meta-heuristic optimization procedure provides realism to the simulation. In this procedure, the flight information obtained from an actual UAV is used to optimize the parameters of the teleoperation algorithm. The quadrotor is adopted as the study case to show the proposal’s effectiveness. Full article

(This article belongs to the Special Issue Perception, Navigation, and Control for Unmanned Aerial Vehicles)

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16 pages, 989 KiB

Open AccessArticle

Enhanced Potential Field-Based Collision Avoidance in Cluttered Three-Dimensional Urban Environments

by Daegyun Choi, Donghoon Kim and Kyuman Lee

Appl. Sci. 2021, 11(22), 11003; https://doi.org/10.3390/app112211003 - 20 Nov 2021

Cited by 4 | Viewed by 1779

Abstract

With the various applications of unmanned aerial vehicles (UAVs), the number of UAVs will increase in limited airspace, leading to an increased risk collision. To reduce such potential risk, this work proposes a collision avoidance strategy for UAVs using an enhanced potential field (EPF) approach in cluttered three-dimensional urban environments. Using the EPF formulated in a two-dimensional environment, the avoidance maneuvers for both horizontal and vertical planes are generated by introducing rotation matrices, and these maneuvers are combined by applying a weighting factor. The numerical simulations with various meaningful scenarios are conducted to validate the performance of the proposed approach. To mimic practical situations, UAV dynamics and sensor limitations were considered. The simulation results show that the proposed approach provides an efficient, reliable, and collision-free path without local minima and unreachable goal issues. Full article

(This article belongs to the Special Issue Perception, Navigation, and Control for Unmanned Aerial Vehicles)

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Review

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24 pages, 3175 KiB

Open AccessReview

Research Status and Development Trend of Underground Intelligent Load-Haul-Dump Vehicle—A Comprehensive Review

by Wei Xiao, Mingxia Liu and Xubing Chen

Appl. Sci. 2022, 12(18), 9290; https://doi.org/10.3390/app12189290 - 16 Sep 2022

Cited by 10 | Viewed by 2066

Abstract

The underground intelligent load-haul-dump vehicle (LHD) is a product of the deep integration of traditional LHD with information network technology, automatic controlling and artificial intelligence technology. It gathers the functions of environmental perception, autonomous driving and fault diagnosis in one machine and exhibits higher safety and greater efficiency than traditional LHD. Hence, it is a particularly important piece of underground mining equipment for building green, safe and smart mines. Taking the studies about intelligent LHD collected by CNKI and WOS databases from 1980 to 2022 as a sample data source, employing Citespace visual analysis software for key feature extraction from the documents, statistical analysis was conducted to clarify the current research progress and the frontier topics of the intelligent LHD academia in the past 40 years, in relation to the future development trends. The development history and application status of underground intelligent LHD was expounded in this article, summarizing the research status at home and abroad from four aspects: ore heap perception and modeling technology, trajectory planning method of bucket shoveling, autonomous navigation technology, real-time monitoring and intelligent fault diagnosis technology. The demerits and merits of the technologies were reviewed as well, with future developing and researching trends of the underground intelligent LHD concluded. Full article

(This article belongs to the Special Issue Perception, Navigation, and Control for Unmanned Aerial Vehicles)

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