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Drones
Special Issues
Mission Planning, Perception and Control for Drones in Wide-Area Operations
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Mission Planning, Perception and Control for Drones in Wide-Area Operations

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drone Design and Development".

Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 3199

Special Issue Editors

Dr. Weiran Yao

E-Mail Website
Guest Editor
Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
Interests: multi-agent systems; path planning and decision; state estimation
Special Issues, Collections and Topics in MDPI journals
Dr. Xiangyu Shao

E-Mail Website
Guest Editor
Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China
Interests: dynamic control of drones; adaptive control; intelligent perception
Dr. Yuehua Liu

E-Mail Website
Guest Editor
School of Computer Engineering and Science, Shanghai University, Shanghai, China
Interests: internet of things analytics; embodied intelligence; robotic technology
Prof. Dr. Liming Xin

E-Mail Website
Guest Editor
School of Computer Engineering and Science, Shanghai University, Shanghai, China
Interests: robotics
Dr. Jiatao Ding

E-Mail Website
Guest Editor
Department of Cognitive Robotics, Faculty of Mechanical Engineering, Delft University of Technology, Delft, The Netherlands
Interests: robotics; optimal control; reinforcement learning

Special Issue Information

Dear Colleagues,

Drones are playing an increasingly important role in both military and civilian fields, especially in wide-area operations. The closed loop, consisting of mission planning, perception, and control, is the key technology to achieve high-level intelligent autonomous wide-area operating drones. Establishing a sophisticated architecture, coordinating the components’ relationships, and achieving information interconnection are essential for reliability and performance of this loop, which brings tremendous emphasis on swarming intelligent, formation planning, control safety, etc.

This Special Issue, entitled Mission Planning, Perception, and Control for Drones in Wide-area Operations, aims to provide a scientific platform regarding the new trends in technologies for drones in wide-area operations, including mission planning technology, artificial intelligence, autonomous intelligent unmanned systems, and the combination of learning and control. We would like to collect innovative research including but not limited to mission planning, reliable perception, decision making, machine learning, and control.

Review and Original Research articles are welcome. Topics of interests include, but are not limited to, the following:

1) Motion control and path planning for wide-area operating drones;

2) Mission planning for wide-area operating drones;

3) Task decomposition and allocation for wide-area operating drones;

4) Simultaneous localization and mapping in a complex environment;

5) Vision recognition and detection;

6) Intelligent perception techniques;

7) Model-based predictive control for intelligent systems;

8) Robust control, sliding mode control, and adaptive control;

9) Deep learning and reinforcement learning;

10) Architecture for learning and control integration;

11) Advanced decision and control methods for unmanned systems;

12) Intelligent swarm and formation.

We look forward to receiving your contributions.

Dr. Weiran Yao
Dr. Xiangyu Shao
Dr. Yuehua Liu
Prof. Dr. Liming Xin
Dr. Jiatao Ding
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

  • motion control and path planning for wide-area operating drones
  • mission planning for wide-area operating drones
  • task decomposition and allocation for wide-area operating drones
  • simultaneous localization and mapping in a complex environment
  • vision recognition and detection
  • intelligent perception techniques
  • model-based predictive control for intelligent systems
  • robust control, sliding mode control, and adaptive control
  • deep learning and reinforcement learning
  • architecture for learning and control integration
  • advanced decision and control methods for unmanned systems
  • intelligent swarm and formation

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

23 pages, 36687 KiB  
Article
UAV–UGV Formation for Delivery Missions: A Practical Case Study
by Leonardo A. Fagundes-Júnior, Celso O. Barcelos, Amanda Piaia Silvatti and Alexandre S. Brandão
Drones 2025, 9(1), 48; https://doi.org/10.3390/drones9010048 - 11 Jan 2025
Viewed by 489
Abstract
Robotic transport missions serve a variety of valuable purposes within similar contexts. These include delivering packages in urban or remote areas, dispatching supplies to disaster or conflict zones, and facilitating delivery operations. In such a context, this work deals with the cooperation and [...] Read more.
Robotic transport missions serve a variety of valuable purposes within similar contexts. These include delivering packages in urban or remote areas, dispatching supplies to disaster or conflict zones, and facilitating delivery operations. In such a context, this work deals with the cooperation and control of multiple-robot systems involving heterogeneous robot formation with sensing and actuation capabilities to perform load transportation tasks. Two off-the-shelf unmanned ground vehicles (UGVs) working cooperatively with one unmanned aerial vehicle (UAV) are used to validate the proposal. The interactions between the UAV and the UGVs are not only information exchanges but also physical couplings required to cooperate in the load’s joint transportation. The existence of an obstacle between the two UGVs makes it impossible for them to meet each other. Thus, the lifting, transport, and delivery of the load from one UGV to the other are performed by a UAV with a suspended electromagnet actuator. Experiments are performed for a weight of 165 g (load + electronic board), which corresponds to up to 36% of the UAV’s mass. Full article
(This article belongs to the Special Issue Mission Planning, Perception and Control for Drones in Wide-Area Operations)
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22 pages, 7862 KiB  
Article
Vision-Based Deep Reinforcement Learning of Unmanned Aerial Vehicle (UAV) Autonomous Navigation Using Privileged Information
by Junqiao Wang, Zhongliang Yu, Dong Zhou, Jiaqi Shi and Runran Deng
Drones 2024, 8(12), 782; https://doi.org/10.3390/drones8120782 - 22 Dec 2024
Viewed by 757
Abstract
The capability of UAVs for efficient autonomous navigation and obstacle avoidance in complex and unknown environments is critical for applications in agricultural irrigation, disaster relief and logistics. In this paper, we propose the DPRL (Distributed Privileged Reinforcement Learning) navigation algorithm, an end-to-end policy [...] Read more.
The capability of UAVs for efficient autonomous navigation and obstacle avoidance in complex and unknown environments is critical for applications in agricultural irrigation, disaster relief and logistics. In this paper, we propose the DPRL (Distributed Privileged Reinforcement Learning) navigation algorithm, an end-to-end policy designed to address the challenge of high-speed autonomous UAV navigation under partially observable environmental conditions. Our approach combines deep reinforcement learning with privileged learning to overcome the impact of observation data corruption caused by partial observability. We leverage an asymmetric Actor–Critic architecture to provide the agent with privileged information during training, which enhances the model’s perceptual capabilities. Additionally, we present a multi-agent exploration strategy across diverse environments to accelerate experience collection, which in turn expedites model convergence. We conducted extensive simulations across various scenarios, benchmarking our DPRL algorithm against state-of-the-art navigation algorithms. The results consistently demonstrate the superior performance of our algorithm in terms of flight efficiency, robustness and overall success rate. Full article
(This article belongs to the Special Issue Mission Planning, Perception and Control for Drones in Wide-Area Operations)
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20 pages, 2951 KiB  
Article
R-LVIO: Resilient LiDAR-Visual-Inertial Odometry for UAVs in GNSS-denied Environment
by Bing Zhang, Xiangyu Shao, Yankun Wang, Guanghui Sun and Weiran Yao
Drones 2024, 8(9), 487; https://doi.org/10.3390/drones8090487 - 14 Sep 2024
Viewed by 1426
Abstract
In low-altitude, GNSS-denied scenarios, Unmanned aerial vehicles (UAVs) rely on sensor fusion for self-localization. This article presents a resilient multi-sensor fusion localization system that integrates light detection and ranging (LiDAR), cameras, and inertial measurement units (IMUs) to achieve state estimation for UAVs. To [...] Read more.
In low-altitude, GNSS-denied scenarios, Unmanned aerial vehicles (UAVs) rely on sensor fusion for self-localization. This article presents a resilient multi-sensor fusion localization system that integrates light detection and ranging (LiDAR), cameras, and inertial measurement units (IMUs) to achieve state estimation for UAVs. To address challenging environments, especially unstructured ones, IMU predictions are used to compensate for pose estimation in the visual and LiDAR components. Specifically, the accuracy of IMU predictions is enhanced by increasing the correction frequency of IMU bias through data integration from the LiDAR and visual modules. To reduce the impact of random errors and measurement noise in LiDAR points on visual depth measurement, cross-validation of visual feature depth is performed using reprojection error to eliminate outliers. Additionally, a structure monitor is introduced to switch operation modes in hybrid point cloud registration, ensuring accurate state estimation in both structured and unstructured environments. In unstructured scenes, a geometric primitive capable of representing irregular planes is employed for point-to-surface registration, along with a novel pose-solving method to estimate the UAV’s pose. Both private and public datasets collected by UAVs validate the proposed system, proving that it outperforms state-of-the-art algorithms by at least 12.6%. Full article
(This article belongs to the Special Issue Mission Planning, Perception and Control for Drones in Wide-Area Operations)
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