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Drones
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Advances in Perception, Communications, and Control for Drones
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Advances in Perception, Communications, and Control for Drones

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

Deadline for manuscript submissions: 10 September 2024 | Viewed by 5067

Special Issue Editors

Dr. Zhihong Liu

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals
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
Special Issues, Collections and Topics in MDPI journals
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

Published Papers (4 papers)

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Research

22 pages, 11606 KiB  
Article
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 - 02 Apr 2024
Viewed by 672
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 [...] Read more.
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  
Article
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 - 01 Apr 2024
Cited by 1 | Viewed by 729
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, [...] Read more.
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  
Article
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
Viewed by 1891
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 [...] Read more.
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  
Article
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 - 01 Sep 2023
Cited by 2 | Viewed by 1169
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 [...] Read more.
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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