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Advances in Perception, Communications, and Control for Drones

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A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: 10 April 2025 | Viewed by 14703

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Special Issue Editors

Dr. Zhihong Liu

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Guest Editor

College of Intelligence and Technology, National University of Defense Technology, Changsha 410073, China
Interests: multi-UAV systems; UAV swarms; cooperative decision and control
Special Issues, Collections and Topics in MDPI journals

Dr. Shihao Yan

E-Mail Website
Guest Editor

School of Science, Edith Cowan University, Perth, Australia 270 Joondalup Drive, Joondalup WA 6027,Australia
Interests: UAV-aided communications; covert communications; covert sensing; location spoofing detection; physical layer security; and IRS-aided wireless communications
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Dr. Yirui Cong

E-Mail Website
Guest Editor

College of Intelligence and Technology, National University of Defense Technology, Changsha 410073, China
Interests: control theory; communication theory; filtering theory
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Dr. Kehao Wang

E-Mail Website
Guest Editor

School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
Interests: stochastic optimization; operation research; scheduling; wireless network communications; embedded operating system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the field of drones has witnessed significant advancements and has broad applications in various industries, including agriculture, delivery services, surveillance, and entertainment. Perception, communications, and control capabilities are the key aspects of autonomy for drones, which enable them to operate tasks in an efficient and intelligent manner without human intervention. Inevitably, drones will also be placed in more challenging conditions and show their great potential in the future, such as observing and understanding complex environments by their sensors on-board, operating path planning and navigation under their perception conditions, multiple or swarms of drones working in a cooperative mode under communication constraints, etc. Quite a few perception, communications, and control problems are still far from being completely solved. We believe recent advancements in this topic could bring a revolution to their capabilities and applications, opening up new possibilities for safer, more efficient, and intelligent operation.

The Special Issue solicits key theoretical and practical contributions to perception, communications, and control for drones, aiming to showcase the latest developments and cutting-edge research in this fast-evolving field.

This Special Issue will welcome manuscripts that link (but not limited to) the following themes:

Advanced perception techniques of object detection and tracking for drones;
Drones remote sensing for mapping and surveying;
Real-time collision detection and avoidance for drones;
Perception-aware target tracking of drones;
Path planning and navigation of drones;
Cooperative control of multiple drones;
Coupling mechanism between control and communication of drones;
Control theory under communication constraint of drones;
Efficient communications for drone swarms;
Robust formation control algorithms of drones;
Communication-oriented control optimization of drones;
Robust or adaptive control design for drones.

We look forward to receiving your original research articles and reviews.

Dr. Zhihong Liu
Dr. Shihao Yan
Dr. Yirui Cong
Dr. Kehao 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

perception
drone communications
autonomous control
communication-oriented control
perception-aware control
drone swarms

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Published Papers (9 papers)

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Research

19 pages, 10725 KiB

Open AccessArticle

Fractional-Order Control Algorithm for Tello EDU Quadrotor Drone Safe Landing during Disturbance on Propeller

by Nurfarah Hanim Binti Rosmadi, Kishore Bingi, P. Arun Mozhi Devan, Reeba Korah, Gaurav Kumar, B Rajanarayan Prusty and Madiah Omar

Drones 2024, 8(10), 566; https://doi.org/10.3390/drones8100566 - 10 Oct 2024

Viewed by 845

Abstract

Quadcopter drones have become increasingly popular because of their versatility and usefulness in various applications, such as surveillance, delivery, and search and rescue operations. Weather conditions and obstacles can undoubtedly pose challenges for drone flights, sometimes causing the loss of one or two propellers. This is a significant challenge as the loss of one or more propellers leads to a sudden loss of control, potentially resulting in a crash, which must be addressed through advanced control strategies. Therefore, this article develops and implements a fractional-order control algorithm to enhance quadrotor drones’ safety and resilience during propeller failure scenarios. The research encompasses the complexities of quadrotor dynamics, fractional-order control theory, and existing methodologies for ensuring safe drone landings. The study emphasizes case validation on experimental results, where four distinct cases were tested using PID and Fractional-order PID (FOPID) controllers. These cases involve various simulated failure conditions to assess the performance and adaptability of the developed control algorithms. The results show the proposed FOPID control’s superior robustness and adaptability compared to traditional PID controllers. These offer significant advancements in navigating dynamic environments and managing disruptive elements introduced during propeller failure simulations in drone control technology. Full article

(This article belongs to the Special Issue Advances in Perception, Communications, and Control for Drones)

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19 pages, 809 KiB

Open AccessArticle

Robust Symbol and Frequency Synchronization Method for Burst OFDM Systems in UAV Communication

by Lintao Li, Yue Han, Zongru Li, Hua Li, Jiayi Lv and Yimin Li

Drones 2024, 8(9), 425; https://doi.org/10.3390/drones8090425 - 25 Aug 2024

Viewed by 793

Abstract

This paper introduces a robust synchronization method for orthogonal frequency division multiplexing (OFDM) in multi-unmanned aerial vehicle (UAV) communication systems, focusing on minimizing overhead while achieving reliable synchronization. The proposed synchronization scheme enhances both frame efficiency and implementation simplicity. Initially, a high-efficiency frame structure is designed without a guard time interval, utilizing a preamble sequence to simultaneously achieve both symbol synchronization and automatic gain control (AGC) before demodulation. Subsequently, a novel 2-bit non-uniform quantization method for the Zadoff–Chu sequences is developed, enabling the correlation operations in the traditional symbol synchronization algorithm to be implemented via bitwise exclusive OR (XOR) and addition operations. The complexity of hardware implementation and the energy consumption for symbol synchronization can be reduced significantly. Furthermore, the impact of AGC on frequency synchronization performance is examined, and an improved frequency synchronization method based on AGC gain compensation is proposed. Finally, the performance of the proposed method is rigorously analyzed and compared with that of the traditional method through computer simulations, demonstrating the effectiveness and superiority of the proposed approach. Full article

(This article belongs to the Special Issue Advances in Perception, Communications, and Control for Drones)

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28 pages, 878 KiB

Open AccessArticle

Optimizing AoI in IoT Networks: UAV-Assisted Data Processing Framework Integrating Cloud–Edge Computing

by Mingfang Ma and Zhengming Wang

Drones 2024, 8(8), 401; https://doi.org/10.3390/drones8080401 - 16 Aug 2024

Viewed by 904

Abstract

Due to the swift development of the Internet of Things (IoT), massive advanced terminals such as sensor nodes have been deployed across diverse applications to sense and acquire surrounding data. Given their limited onboard capabilities, these terminals tend to offload data to servers for further processing. However, terminals cannot transmit data directly in regions with restricted communication infrastructure. With the increasing proliferation of unmanned aerial vehicles (UAVs), they have become instrumental in collecting and transmitting data from the region to servers. Nevertheless, because of the energy constraints and time-consuming nature of data processing by UAVs, it becomes imperative not only to utilize multiple UAVs to traverse a large-scale region and collect data, but also to overcome the substantial challenge posed by the time sensitivity of data information. Therefore, this paper introduces the important indicator Age of Information (AoI) that measures data freshness, and develops an intelligent AoI optimization data processing approach named AODP in a hierarchical cloud–edge architecture. In the proposed AODP, we design a management mechanism through the formation of clusters by terminals and the service associations between terminals and hovering positions (HPs). To further improve collection efficiency of UAVs, an HP clustering strategy is developed to construct the UAV-HP association. Finally, under the consideration of energy supply, time tolerance, and flexible computing modes, a gray wolf optimization algorithm-based multi-objective path planning scheme is proposed, achieving both average and peak AoI minimization. Simulation results demonstrate that the AODP can converge well, guarantee reliable AoI, and exhibit superior performance compared to existing solutions in multiple scenarios. Full article

(This article belongs to the Special Issue Advances in Perception, Communications, and Control for Drones)

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22 pages, 9519 KiB

Open AccessArticle

Multi-Level Switching Control Scheme for Folding Wing VTOL UAV Based on Dynamic Allocation

by Zehuai Lin, Binbin Yan, Tong Zhang, Shaoyi Li, Zhongjie Meng and Shuangxi Liu

Drones 2024, 8(7), 303; https://doi.org/10.3390/drones8070303 - 7 Jul 2024

Cited by 1 | Viewed by 1026

Abstract

A folding wing vertical take-off and landing (VTOL) UAV is capable of transitioning between quadrotor and fixed-wing modes, but significant alterations occur in its dynamics model and maneuvering mode during the transformation process, thereby imposing greater demands on the adaptability of its control system. In this paper, a multi-level switching control scheme based on dynamic allocation is proposed for the deformation stage. Firstly, according to the physical characteristics of the wing folding mechanism, a dynamic model is established. The influence of the incoming flow on the rotors is considered, and the dynamic coupling characteristics in its transition process are analyzed. Secondly, by inverting the changes in rotor position and axial direction, a dynamic allocation algorithm for the rotors is designed. Then, the quadrotor controller and the fixed-wing controller are switched and mixed in multiple loops to form a multi-level switching control scheme. Finally, the simulation results show that the designed multi-level switching control scheme is effective and robust in forward and backward deformation processes, and its anti-interference ability is stronger compared with that of the control scheme without dynamic allocation. Full article

(This article belongs to the Special Issue Advances in Perception, Communications, and Control for Drones)

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23 pages, 30652 KiB

Open AccessArticle

EUAVDet: An Efficient and Lightweight Object Detector for UAV Aerial Images with an Edge-Based Computing Platform

by Wanneng Wu, Ao Liu, Jianwen Hu, Yan Mo, Shao Xiang, Puhong Duan and Qiaokang Liang

Drones 2024, 8(6), 261; https://doi.org/10.3390/drones8060261 - 13 Jun 2024

Cited by 4 | Viewed by 1599

Abstract

Crafting an edge-based real-time object detector for unmanned aerial vehicle (UAV) aerial images is challenging because of the limited computational resources and the small size of detected objects. Existing lightweight object detectors often prioritize speed over detecting extremely small targets. To better balance this trade-off, this paper proposes an efficient and low-complexity object detector for edge computing platforms deployed on UAVs, termed EUAVDet (Edge-based UAV Object Detector). Specifically, an efficient feature downsampling module and a novel multi-kernel aggregation block are first introduced into the backbone network to retain more feature details and capture richer spatial information. Subsequently, an improved feature pyramid network with a faster ghost module is incorporated into the neck network to fuse multi-scale features with fewer parameters. Experimental evaluations on the VisDrone, SeaDronesSeeV2, and UAVDT datasets demonstrate the effectiveness and plug-and-play capability of our proposed modules. Compared with the state-of-the-art YOLOv8 detector, the proposed EUAVDet achieves better performance in nearly all the metrics, including parameters, FLOPs, mAP, and FPS. The smallest version of EUAVDet (EUAVDet-n) contains only 1.34 M parameters and achieves over 20 fps on the Jetson Nano. Our algorithm strikes a better balance between detection accuracy and inference speed, making it suitable for edge-based UAV applications. Full article

(This article belongs to the Special Issue Advances in Perception, Communications, and Control for Drones)

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22 pages, 11606 KiB

Open AccessArticle

Online Predictive Visual Servo Control for Constrained Target Tracking of Fixed-Wing Unmanned Aerial Vehicles

by Lingjie Yang, Xiangke Wang, Yu Zhou, Zhihong Liu and Lincheng Shen

Drones 2024, 8(4), 136; https://doi.org/10.3390/drones8040136 - 2 Apr 2024

Cited by 3 | Viewed by 1551

Abstract

This paper proposes an online predictive control method for fixed-wing unmanned aerial vehicles (UAVs) with a pan-tilt camera in target tracking. It aims to achieve long-term tracking while concurrently maintaining the target near the image center. Particularly, this work takes the UAV and pan-tilt camera as an overall system and deals with the target tracking problem via joint optimization, so that the tracking ability of the UAV can be improved. The image captured by the pan-tilt camera is the unique input associated with the target, and model predictive control (MPC) is used to solve the optimization problem with constraints that cannot be performed by the classic image-based visual servoing (IBVS). In addition to the dynamic constraint of the UAV, the perception constraint of the camera is also taken into consideration, which is described by the maximum distance between the target and the camera. The accurate detection of the target depends on the amount of its feature information contained in the image, which is highly related to the relative distance between the target and the camera. Moreover, considering the real-time requirements of practical applications, an MPC strategy based on soft constraints and a warm start is presented. Furthermore, a switching-based approach is proposed to return the target back to the perception range quickly once it exceeds the range, and the exponential asymptotic stability of the switched controller is proven as well. Both numerical and hardware-in-the-loop (HITL) simulations are conducted to verify the effectiveness and superiority of the proposed method compared with the existing method. Full article

(This article belongs to the Special Issue Advances in Perception, Communications, and Control for Drones)

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33 pages, 10623 KiB

Open AccessArticle

UAV Swarm Search Path Planning Method Based on Probability of Containment

by Xiangyu Fan, Hao Li, You Chen and Danna Dong

Drones 2024, 8(4), 132; https://doi.org/10.3390/drones8040132 - 1 Apr 2024

Cited by 5 | Viewed by 1762

Abstract

To improve the search efficiency of the unmanned aerial vehicle (UAV) swarm in disaster areas, the target distribution probability graph in the prior information is introduced, and a drone cluster search trajectory planning method based on probability of containment (POC) is proposed. Firstly, based on the concept of probability of containment in search theory, a task area division method for polygonal and circular areas is constructed, and the corresponding search trajectory is constructed. Then, the influence of factors, including probability of containment, probability of detection, and probability of success on search efficiency, is sorted out, and the objective function of search trajectory optimization is constructed. Subsequently, an adaptive mutation operator is used to improve the differential evolution algorithm, thus constructing a trajectory optimization process based on the improved adaptive differential evolution algorithm. Through simulation verification, the proposed method can achieve a full coverage search of the task area and a rapid search within a limited time, and can prioritize the coverage of areas with a high target existence probability as much as possible to achieve a higher cumulative success probability. Moreover, the time efficiency and accuracy of the solution are high. Full article

(This article belongs to the Special Issue Advances in Perception, Communications, and Control for Drones)

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13 pages, 2129 KiB

Open AccessArticle

High-Performance Detection-Based Tracker for Multiple Object Tracking in UAVs

by Xi Li, Ruixiang Zhu, Xianguo Yu and Xiangke Wang

Drones 2023, 7(11), 681; https://doi.org/10.3390/drones7110681 - 20 Nov 2023

Cited by 3 | Viewed by 3003

Abstract

As a result of increasing urbanization, traffic monitoring in cities has become a challenging task. The use of Unmanned Aerial Vehicles (UAVs) provides an attractive solution to this problem. Multi-Object Tracking (MOT) for UAVs is a key technology to fulfill this task. Traditional detection-based-tracking (DBT) methods begin by employing an object detector to retrieve targets in each image and then track them based on a matching algorithm. Recently, the popular multi-task learning methods have been dominating this area, since they can detect targets and extract Re-Identification (Re-ID) features in a computationally efficient way. However, the detection task and the tracking task have conflicting requirements on image features, leading to the poor performance of the joint learning model compared to separate detection and tracking methods. The problem is more severe when it comes to UAV images due to the presence of irregular motion of a large number of small targets. In this paper, we propose using a balanced Joint Detection and Re-ID learning (JDR) network to address the MOT problem in UAV vision. To better handle the non-uniform motion of objects in UAV videos, the Set-Membership Filter is applied, which describes object state as a bounded set. An appearance-matching cascade is then proposed based on the target state set. Furthermore, a Motion-Mutation module is designed to address the challenges posed by the abrupt motion of UAV. Extensive experiments on the VisDrone2019-MOT dataset certify that our proposed model, referred to as SMFMOT, outperforms the state-of-the-art models by a wide margin and achieves superior performance in the MOT tasks in UAV videos. Full article

(This article belongs to the Special Issue Advances in Perception, Communications, and Control for Drones)

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22 pages, 2473 KiB

Open AccessArticle

Hierarchical Task Assignment for Multi-UAV System in Large-Scale Group-to-Group Interception Scenarios

by Xinning Wu, Mengge Zhang, Xiangke Wang, Yongbin Zheng and Huangchao Yu

Drones 2023, 7(9), 560; https://doi.org/10.3390/drones7090560 - 1 Sep 2023

Cited by 3 | Viewed by 1774

Abstract

The multi-UAV task assignment problem in large-scale group-to-group interception scenarios presents challenges in terms of large computational complexity and the lack of accurate evaluation models. This paper proposes an effective evaluation model and hierarchical task assignment framework to address these challenges. The evaluation model incorporates the dynamics constraints specific to fixed-wing UAVs and improves the Apollonius circle model to accurately describe the cooperative interception effectiveness of multiple UAVs. By evaluating the interception effectiveness during the interception process, the assignment scheme of the multiple UAVs could be given based on the model. To optimize the configuration of UAVs and targets, a hierarchical framework based on the network flow algorithm is employed. This framework utilizes a clustering method based on feature similarity and interception advantage to decompose the large-scale task assignment problem into smaller, complete submodels. Following the assignment, Dubins curves are planned to the optimal interception points, ensuring the effectiveness of the interception task. Simulation results demonstrate the feasibility and effectiveness of the proposed scheme. With the increase in the model scale, the proposed scheme has a greater descending rate of runtime. In a large-scale scenario involving 200 UAVs and 100 targets, the runtime is reduced by

84.86 %

. Full article

(This article belongs to the Special Issue Advances in Perception, Communications, and Control for Drones)

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