Nonlinear and Optimal, Real-Time Control of UAV

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Automation and Control Systems".

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 5978

Special Issue Editors


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Guest Editor
Computer Vision and Aerial Robotics (CVAR) Group, Centre for Automation and Robotics (UPM-CSIC), Universidad Politécnica de Madrid, Calle José Gutiérrez Abascal 2, 28006 Madrid, Spain
Interests: UAVs; object tracking; visual control and guidance; visual SLAM; stereo and omnidirectional vision; aerial robotics; computer vision; machine learning
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Guest Editor
Faculty of Electrical Engineering, Institute of Control and Information Engineering, Poznan University of Technology, Poznan, Poland
Interests: UAVs; optimization; robust control; adaptive control; optimal control; control theory; modelling and identification
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Institute of Robotics and Machine Intelligence, Faculty of Control, Robotics and Electrical Engineering, Poznan University of Technology, 60-965 Poznan, Poland
Interests: UAV control and simulation; machine learning for UAV autonomous control; motion and mission planning; autonomy reliability and safety of UAVs; multi-robot systems; swarm robotics; relative UAV localization; object tracking; perception and multi-sensor fusion; optimization techniques for UAVs
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid development and growth in the field of UAVs as a versatile tool for monitoring, last-centimeter delivery systems, inspection, interception, photography systems, and advances in the miniaturization of their instrumentation, have given rise to widespread deployment in virtually all areas of science.

This Special Issue is seeking submissions that highlight advances in the development and use of nonlinear and optimal real-time control of UAVs. We invite articles concerning all aspects of problems involving UAV services, including data processing and sensor fusion for control purposes, obstacle and collision avoidance, trajectory generation for single UAVs or swarms of UAVs, communications and networks among UAVs, and mission planning.

This topic is coherent with the scope of Machines, as it covers applications of automation, systems and control engineering, computer or mechanical engineering issues, and robotics. 

Prof. Dr. Pascual Campoy
Assoc. Prof. Dr. Dariusz Horla
Dr. Wojciech Giernacki
Guest Editors

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Keywords

  • UAV control
  • motion planning
  • formation control
  • mission planning
  • sensor fusion
  • swarms of UAVs
  • nonlinear controllers
  • optimization issues in UAVs

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Published Papers (2 papers)

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Research

14 pages, 2893 KiB  
Article
Event-Triggered Intervention Framework for UAV-UGV Coordination Systems
by Wu Wang, Junyou Guo, Guoqing Tian, Yutao Chen and Jie Huang
Machines 2021, 9(12), 371; https://doi.org/10.3390/machines9120371 - 20 Dec 2021
Cited by 5 | Viewed by 2800
Abstract
Air-ground coordination systems are usually composed of unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV). In such a system, UAVs can utilize their much more perceptive information to plan the path for UGVs. However, the correctness and accuracy of the planned route [...] Read more.
Air-ground coordination systems are usually composed of unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV). In such a system, UAVs can utilize their much more perceptive information to plan the path for UGVs. However, the correctness and accuracy of the planned route are often not guaranteed, and the communication and computation burdens increase with more sophisticated algorithms. This paper proposes a new type of air-ground coordination framework to enable UAVs intervention into UGVs tasks. An event-triggered mechanism in the null space behavior control (NSBC) framework is proposed to decide if an intervention is necessary and the timing of the intervention. Then, the problem of whether to accept the intervention is formulated as an integer programming problem and is solved using model predictive control (MPC). Simulation results show that the UAV can intervene in UGVs accurately and on time, and the UGVs can effectively decide whether to accept the intervention to get rid of troubles, thereby improving the intelligence of the air-ground coordination system. Full article
(This article belongs to the Special Issue Nonlinear and Optimal, Real-Time Control of UAV)
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15 pages, 3406 KiB  
Article
Fast Attitude Estimation System for Unmanned Ground Vehicle Based on Vision/Inertial Fusion
by Zhenhui Fan, Pengxiang Yang, Chunbo Mei, Qiju Zhu and Xiao Luo
Machines 2021, 9(10), 241; https://doi.org/10.3390/machines9100241 - 18 Oct 2021
Cited by 3 | Viewed by 1877
Abstract
The attitude estimation system based on vision/inertial fusion is of vital importance and great urgency for unmanned ground vehicles (UGVs) in GNSS-challenged/denied environments. This paper aims to develop a fast vision/inertial fusion system to estimate attitude; which can provide attitude estimation for UGVs [...] Read more.
The attitude estimation system based on vision/inertial fusion is of vital importance and great urgency for unmanned ground vehicles (UGVs) in GNSS-challenged/denied environments. This paper aims to develop a fast vision/inertial fusion system to estimate attitude; which can provide attitude estimation for UGVs during long endurance. The core idea in this paper is to integrate the attitude estimated by continuous vision with the inertial pre-integration results based on optimization. Considering that the time-consuming nature of the classical methods comes from the optimization and maintenance of 3D feature points in the back-end optimization thread, the continuous vision section calculates the attitude by image matching without reconstructing the environment. To tackle the cumulative error of the continuous vision and inertial pre-integration, the prior attitude information is introduced for correction, which is measured and labeled by an off-line fusion of multi-sensors. Experiments with the open-source datasets and in road environments have been carried out, and the results show that the average attitude errors are 1.11° and 1.96°, respectively. The road test results demonstrate that the processing time per frame is 24 ms, which shows that the proposed system improves the computational efficiency. Full article
(This article belongs to the Special Issue Nonlinear and Optimal, Real-Time Control of UAV)
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