Special Issue "Autonomous Control of Unmanned Aerial Vehicles"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: 31 December 2018

Special Issue Editor

Guest Editor
Prof. Victor Becerra

School of Engineering, University of Portsmouth, Anglesea Building, Anglesea Road, Portsmouth, PO1 3DJ, United Kingdom
Website | E-Mail
Interests: computational optimal control; fault diagnosis; fault tolerant control; autonomous control systems; state estimation; control of power systems; control of energy storage

Special Issue Information

Dear Colleagues,

Unmanned aerial vehicles are being increasingly used in different applications in both military and civilian domains. Their missions include, but are not limited to, surveillance, reconnaissance, target acquisition, border patrol, highway monitoring, aerial imaging, industrial inspection, etc. Operating unmanned flying vehicles is useful yet it can be challenging when the vehicle interacts with the environment. This interaction could be, for instance, in the form of landing on ground or landing pads, docking into a station, approaching terrain for inspection, or approaching another aircraft for refueling purposes. Such tasks are can be solved when the vehicle is remotely piloted, especially when the pilot has a first-person view of the environment, however, this might not always be possible due to the unavailability of a suitable data link or when there are long delays on the data link. Thus, it is important to find effective and flexible strategies to enable vehicles to perform such tasks autonomously.

Classical features of autonomous control design involve stability enhancement and waypoint flight. However, new requirements in the recent development of UAVs demand robust and adaptive control techniques for different flight conditions, aggressive maneuvers, use of non-traditional sensors such as cameras, obstacle avoidance, fault detection, fault tolerant control, etc.  To achieve these ambitious requirements, systematic and innovative methods are required.

The aim of this Special Issue is to bring together researchers and practitioners in the field of unmanned aerial systems, with a common interest in autonomous control development. The Special Issue will present key challenges associated with autonomous control of unmanned aerial vehicles and will propose solution methodologies to address such challenges. We invite original contributions, as well as review papers in this area.

Prof. Victor Becerra
Guest Editor

Manuscript Submission Information

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Keywords

  • unmanned aerial vehicles
  • autonomous control systems
  • autonomous vehicles
  • flying robots

Published Papers (6 papers)

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Research

Open AccessArticle Research on Air Confrontation Maneuver Decision-Making Method Based on Reinforcement Learning
Electronics 2018, 7(11), 279; https://doi.org/10.3390/electronics7110279
Received: 26 September 2018 / Revised: 17 October 2018 / Accepted: 22 October 2018 / Published: 27 October 2018
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Abstract
With the development of information technology, the degree of intelligence in air confrontation is increasing, and the demand for automated intelligent decision-making systems is becoming more intense. Based on the characteristics of over-the-horizon air confrontation, this paper constructs a super-horizon air confrontation training
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With the development of information technology, the degree of intelligence in air confrontation is increasing, and the demand for automated intelligent decision-making systems is becoming more intense. Based on the characteristics of over-the-horizon air confrontation, this paper constructs a super-horizon air confrontation training environment, which includes aircraft model modeling, air confrontation scene design, enemy aircraft strategy design, and reward and punishment signal design. In order to improve the efficiency of the reinforcement learning algorithm for the exploration of strategy space, this paper proposes a heuristic Q-Network method that integrates expert experience, and uses expert experience as a heuristic signal to guide the search process. At the same time, heuristic exploration and random exploration are combined. Aiming at the over-the-horizon air confrontation maneuver decision problem, the heuristic Q-Network method is adopted to train the neural network model in the over-the-horizon air confrontation training environment. Through continuous interaction with the environment, self-learning of the air confrontation maneuver strategy is realized. The efficiency of the heuristic Q-Network method and effectiveness of the air confrontation maneuver strategy are verified by simulation experiments. Full article
(This article belongs to the Special Issue Autonomous Control of Unmanned Aerial Vehicles)
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Open AccessArticle Multiple UAV Systems for Agricultural Applications: Control, Implementation, and Evaluation
Electronics 2018, 7(9), 162; https://doi.org/10.3390/electronics7090162
Received: 24 July 2018 / Revised: 9 August 2018 / Accepted: 22 August 2018 / Published: 24 August 2018
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Abstract
The introduction of multiple unmanned aerial vehicle (UAV) systems into agriculture causes an increase in work efficiency and a decrease in operator fatigue. However, systems that are commonly used in agriculture perform tasks using a single UAV with a centralized controller. In this
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The introduction of multiple unmanned aerial vehicle (UAV) systems into agriculture causes an increase in work efficiency and a decrease in operator fatigue. However, systems that are commonly used in agriculture perform tasks using a single UAV with a centralized controller. In this study, we develop a multi-UAV system for agriculture using the distributed swarm control algorithm and evaluate the performance of the system. The performance of the proposed agricultural multi-UAV system is quantitatively evaluated and analyzed through four experimental cases: single UAV with autonomous control, multiple UAVs with autonomous control, single UAV with remote control, and multiple UAVs with remote control. Moreover, the performance of each system was analyzed through seven performance metrics: total time, setup time, flight time, battery consumption, inaccuracy of land, haptic control effort, and coverage ratio. Experimental results indicate that the performance of the multi-UAV system is significantly superior to the single-UAV system. Full article
(This article belongs to the Special Issue Autonomous Control of Unmanned Aerial Vehicles)
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Open AccessArticle Super-Twisting Extended State Observer and Sliding Mode Controller for Quadrotor UAV Attitude System in Presence of Wind Gust and Actuator Faults
Electronics 2018, 7(8), 128; https://doi.org/10.3390/electronics7080128
Received: 19 June 2018 / Revised: 9 July 2018 / Accepted: 24 July 2018 / Published: 26 July 2018
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Abstract
This article addresses the problem of high precision attitude control for quadrotor unmanned aerial vehicle in presence of wind gust and actuator faults. We consider the effect of those factors as lumped disturbances, and in order to realize the quickly and accurately estimation
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This article addresses the problem of high precision attitude control for quadrotor unmanned aerial vehicle in presence of wind gust and actuator faults. We consider the effect of those factors as lumped disturbances, and in order to realize the quickly and accurately estimation of the disturbances, we propose a control strategy based on the online disturbance uncertainty estimation and attenuation method. Firstly, an enhanced extended state observer (ESO) is constructed based on the super-twisting (ST) algorithm to estimate and attenuate the impact of wind gust and actuator faults in finite time. And the convergence analysis and parameter selection rule of STESO are given following. Secondly, in order to guarantee the asymptotic convergence of desired attitude timely, a sliding mode control law is derived based on the super-twisting algorithm. And a comprehensive stability analysis for the entire system is presented based on the Lyapunov stability theory. Finally, to demonstrate the efficiency of the proposed solution, numerical simulations and real time experiments are carried out in presences of wind disturbance and actuator faults. Full article
(This article belongs to the Special Issue Autonomous Control of Unmanned Aerial Vehicles)
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Open AccessArticle Harmonic Extended State Observer Based Anti-Swing Attitude Control for Quadrotor with Slung Load
Electronics 2018, 7(6), 83; https://doi.org/10.3390/electronics7060083
Received: 8 May 2018 / Revised: 27 May 2018 / Accepted: 28 May 2018 / Published: 29 May 2018
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Abstract
During the flight of the quadrotor, the existence of a slung load will exert a swing effect on the system and the motion of which will significantly change the dynamics of the quadrotor. The external torque caused by the slung load can be
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During the flight of the quadrotor, the existence of a slung load will exert a swing effect on the system and the motion of which will significantly change the dynamics of the quadrotor. The external torque caused by the slung load can be considered as a kind of disturbance and it is a threat to the attitude control stability of the system. In order to solve this problem, a high precision disturbance compensation method is presented in this paper, based on the harmonic extended state observer (HESO). Firstly, a generic mathematical model for the quadrotor-slung load system is obtained via the Lagrangian mechanics, and according to the analysis of the slung load motion, we obtain the disturbance as a form of periodic equation. Secondly, based on the dynamic model of the disturbance, we propose a HESO to achieve high precision disturbance estimation and its stability is proved by Lyapunov theory. Thirdly, we designed an attitude tracking controller based on backstepping method, and discussed the stability of the entire system. Finally, numerical simulations and real time experiments are carried out to evaluate the performance of the proposed method. Our results show that the robustness of the quadrotor subject to slung load has been improved. Full article
(This article belongs to the Special Issue Autonomous Control of Unmanned Aerial Vehicles)
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Open AccessArticle Real-Time Ground Vehicle Detection in Aerial Infrared Imagery Based on Convolutional Neural Network
Electronics 2018, 7(6), 78; https://doi.org/10.3390/electronics7060078
Received: 3 April 2018 / Revised: 16 May 2018 / Accepted: 19 May 2018 / Published: 23 May 2018
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Abstract
An infrared sensor is a commonly used imaging device. Unmanned aerial vehicles, the most promising moving platform, each play a vital role in their own field, respectively. However, the two devices are seldom combined in automatic ground vehicle detection tasks. Therefore, how to
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An infrared sensor is a commonly used imaging device. Unmanned aerial vehicles, the most promising moving platform, each play a vital role in their own field, respectively. However, the two devices are seldom combined in automatic ground vehicle detection tasks. Therefore, how to make full use of them—especially in ground vehicle detection based on aerial imagery–has aroused wide academic concern. However, due to the aerial imagery’s low-resolution and the vehicle detection’s complexity, how to extract remarkable features and handle pose variations, view changes as well as surrounding radiation remains a challenge. In fact, these typical abstract features extracted by convolutional neural networks are more recognizable than the engineering features, and those complex conditions involved can be learned and memorized before. In this paper, a novel approach towards ground vehicle detection in aerial infrared images based on a convolutional neural network is proposed. The UAV and the infrared sensor used in this application are firstly introduced. Then, a novel aerial moving platform is built and an aerial infrared vehicle dataset is unprecedentedly constructed. We publicly release this dataset (NPU_CS_UAV_IR_DATA), which can be used for the following research in this field. Next, an end-to-end convolutional neural network is built. With large amounts of recognized features being iteratively learned, a real-time ground vehicle model is constructed. It has the unique ability to detect both the stationary vehicles and moving vehicles in real urban environments. We evaluate the proposed algorithm on some low–resolution aerial infrared images. Experiments on the NPU_CS_UAV_IR_DATA dataset demonstrate that the proposed method is effective and efficient to recognize the ground vehicles. Moreover it can accomplish the task in real-time while achieving superior performances in leak and false alarm ratio. Full article
(This article belongs to the Special Issue Autonomous Control of Unmanned Aerial Vehicles)
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Open AccessArticle Monocular Vision SLAM-Based UAV Autonomous Landing in Emergencies and Unknown Environments
Electronics 2018, 7(5), 73; https://doi.org/10.3390/electronics7050073
Received: 17 April 2018 / Revised: 9 May 2018 / Accepted: 11 May 2018 / Published: 15 May 2018
Cited by 1 | PDF Full-text (7498 KB) | HTML Full-text | XML Full-text
Abstract
With the popularization and wide application of drones in military and civilian fields, the safety of drones must be considered. At present, the failure and drop rates of drones are still much higher than those of manned aircraft. Therefore, it is imperative to
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With the popularization and wide application of drones in military and civilian fields, the safety of drones must be considered. At present, the failure and drop rates of drones are still much higher than those of manned aircraft. Therefore, it is imperative to improve the research on the safe landing and recovery of drones. However, most drone navigation methods rely on global positioning system (GPS) signals. When GPS signals are missing, these drones cannot land or recover properly. In fact, with the help of optical equipment and image recognition technology, the position and posture of the drone in three dimensions can be obtained, and the environment where the drone is located can be perceived. This paper proposes and implements a monocular vision-based drone autonomous landing system in emergencies and in unstructured environments. In this system, a novel map representation approach is proposed that combines three-dimensional features and a mid-pass filter to remove noise and construct a grid map with different heights. In addition, a region segmentation is presented to detect the edges of different-height grid areas for the sake of improving the speed and accuracy of the subsequent landing area selection. As a visual landing technology, this paper evaluates the proposed algorithm in two tasks: scene reconstruction integrity and landing location security. In these tasks, firstly, a drone scans the scene and acquires key frames in the monocular visual simultaneous localization and mapping (SLAM) system in order to estimate the pose of the drone and to create a three-dimensional point cloud map. Then, the filtered three-dimensional point cloud map is converted into a grid map. The grid map is further divided into different regions to select the appropriate landing zone. Thus, it can carry out autonomous route planning. Finally, when it stops upon the landing field, it will start the descent mode near the landing area. Experiments in multiple sets of real scenes show that the environmental awareness and the landing area selection have high robustness and real-time performance. Full article
(This article belongs to the Special Issue Autonomous Control of Unmanned Aerial Vehicles)
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