Drones

37 pages, 38902 KiB

Open AccessArticle

Differentiator- and Observer-Based Feedback Linearized Advanced Nonlinear Control Strategies for an Unmanned Aerial Vehicle System

by Saqib Irfan, Liangyu Zhao, Safeer Ullah, Usman Javaid and Jamshed Iqbal

Drones 2024, 8(10), 527; https://doi.org/10.3390/drones8100527 (registering DOI) - 26 Sep 2024

Abstract

This paper presents novel chattering-free robust control strategies for addressing disturbances and uncertainties in a two-degree-of-freedom (2-DOF) unmanned aerial vehicle (UAV) dynamic model, with a focus on the highly nonlinear and strongly coupled nature of the system. The novelty lies in the development [...] Read more.

This paper presents novel chattering-free robust control strategies for addressing disturbances and uncertainties in a two-degree-of-freedom (2-DOF) unmanned aerial vehicle (UAV) dynamic model, with a focus on the highly nonlinear and strongly coupled nature of the system. The novelty lies in the development of sliding mode control (SMC), integral sliding mode control (ISMC), and terminal sliding mode control (TSMC) laws specifically tailored for the twin-rotor MIMO system (TRMS). These strategies are validated through both simulation and real-time experiments. A key contribution is the introduction of a uniform robust exact differentiator (URED) to recover rotor speed and missing derivatives, combined with a nonlinear state feedback observer to improve system observability. A feedback linearization approach, using lie derivatives and diffeomorphism principles, is employed to decouple the system into horizontal and vertical subsystems. Comparative analysis of the transient performance of the proposed controllers, with respect to metrics such as settling time, overshoot, rise time, and steady-state errors, is provided. The ISMC method, in particular, effectively mitigates the chattering issue prevalent in traditional SMC, improving both system performance and actuator longevity. Experimental results on the TRMS demonstrate the superior tracking performance and robustness of the proposed control laws in the presence of nonlinearities, uncertainties, and external disturbances. This research contributes a comprehensive control design framework with proven real-time implementation, offering significant advancements over existing methodologies. Full article

(This article belongs to the Special Issue Conceptual Design, Modeling, and Control Strategies of Drones 3rd Edition)

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23 pages, 2059 KiB

Open AccessArticle

The Role of Environments and Sensing Strategies in Unmanned Aerial Vehicle Crowdsensing

by Yaqiong Zhou, Cong Hu, Yong Zhao, Zhengqiu Zhu, Rusheng Ju and Sihang Qiu

Drones 2024, 8(10), 526; https://doi.org/10.3390/drones8100526 - 26 Sep 2024

Abstract

Crowdsensing has gained popularity across various domains such as urban transportation, environmental monitoring, and public safety. Unmanned aerial vehicle (UAV) crowdsensing is a novel approach that collects extensive data from targeted environments using UAVs equipped with built-in sensors. Unlike conventional methods that rely [...] Read more.

Crowdsensing has gained popularity across various domains such as urban transportation, environmental monitoring, and public safety. Unmanned aerial vehicle (UAV) crowdsensing is a novel approach that collects extensive data from targeted environments using UAVs equipped with built-in sensors. Unlike conventional methods that rely on fixed sensor networks or the mobility of humans, UAV crowdsensing offers high flexibility and scalability. With the rapid advancement of artificial intelligence techniques, UAV crowdsensing is becoming increasingly intelligent and autonomous. Previous studies on UAV crowdsensing have predominantly focused on algorithmic sensing strategies without considering the impact of different sensing environments. Thus, there is a research gap regarding the influence of environmental factors and sensing strategies in this field. To this end, we designed a

4 \times 3

empirical study, classifying sensing environments into four major categories: open, urban, natural, and indoor. We conducted experiments to understand how these environments influence three typical crowdsensing strategies: opportunistic, algorithmic, and collaborative. The statistical results reveal significant differences in both environments and sensing strategies. We found that an algorithmic strategy (machine-only) is suitable for open and natural environments, while a collaborative strategy (human and machine) is ideal for urban and indoor environments. This study has crucial implications for adopting appropriate sensing strategies for different environments of UAV crowdsensing tasks. Full article

17 pages, 568 KiB

Open AccessArticle

Potential and Challenges in Airborne Automated External Defibrillator Delivery by Drones in a Mountainous Region

by Christian Wankmüller, Ursula Rohrer, Philip Fischer, Patrick Nürnberger and Ewald Kolesnik

Drones 2024, 8(10), 525; https://doi.org/10.3390/drones8100525 - 26 Sep 2024

Abstract

Delivering an automated external defibrillator (AED) to a patient suffering from out-of-hospital cardiac arrest (OHCA) as quickly as possible is a critical task. In this field, airborne drones may help to overcome long response times, especially in mountainous regions where topography and weather [...] Read more.

Delivering an automated external defibrillator (AED) to a patient suffering from out-of-hospital cardiac arrest (OHCA) as quickly as possible is a critical task. In this field, airborne drones may help to overcome long response times, especially in mountainous regions where topography and weather pose several challenges for rescuers. Drones are considered a fast option to shorten the time to the first AED shock. This study presents insights into the safety regulations, performance, reliability and public perception of this specific drone-based application. The findings are based on field tests that focused on the operational/logistical benefits and challenges of semi-autonomous drone-based AED delivery to simulated emergency sites in mountainous terrain. The generated results underline the operational and technical feasibility of the proposed system given successful AED delivery in all simulation scenarios. Several challenges remain, such as improvements in terms of the AED pick-up, mobile phone connectivity, tracking of GPS coordinates and weather resistance of the used drone are required. Overall, the study supports paving the way for future trials and real-world implementations of drones into existing emergency response systems. Full article

(This article belongs to the Special Issue Application of Drones in Medicine and Healthcare)

27 pages, 10867 KiB

Open AccessArticle

Interception of a Single Intruding Unmanned Aerial Vehicle by Multiple Missiles Using the Novel EA-MADDPG Training Algorithm

by He Cai, Xingsheng Li, Yibo Zhang and Huanli Gao

Drones 2024, 8(10), 524; https://doi.org/10.3390/drones8100524 - 26 Sep 2024

Abstract

This paper proposes an improved multi-agent deep deterministic policy gradient algorithm called the equal-reward and action-enhanced multi-agent deep deterministic policy gradient (EA-MADDPG) algorithm to solve the guidance problem of multiple missiles cooperating to intercept a single intruding UAV in three-dimensional space. The key [...] Read more.

This paper proposes an improved multi-agent deep deterministic policy gradient algorithm called the equal-reward and action-enhanced multi-agent deep deterministic policy gradient (EA-MADDPG) algorithm to solve the guidance problem of multiple missiles cooperating to intercept a single intruding UAV in three-dimensional space. The key innovations of EA-MADDPG include the implementation of the action filter with additional reward functions, optimal replay buffer, and equal reward setting. The additional reward functions and the action filter are set to enhance the exploration performance of the missiles during training. The optimal replay buffer and the equal reward setting are implemented to improve the utilization efficiency of exploration experiences obtained through the action filter. In order to prevent over-learning from certain experiences, a special storage mechanism is established, where experiences obtained through the action filter are stored only in the optimal replay buffer, while normal experiences are stored in both the optimal replay buffer and normal replay buffer. Meanwhile, we gradually reduce the selection probability of the action filter and the sampling ratio of the optimal replay buffer. Finally, comparative experiments show that the algorithm enhances the agents’ exploration capabilities, allowing them to learn policies more quickly and stably, which enables multiple missiles to complete the interception task more rapidly and with a higher success rate. Full article

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26 pages, 11965 KiB

Open AccessArticle

AMFEF-DETR: An End-to-End Adaptive Multi-Scale Feature Extraction and Fusion Object Detection Network Based on UAV Aerial Images

by Sen Wang, Huiping Jiang, Jixiang Yang, Xuan Ma and Jiamin Chen

Drones 2024, 8(10), 523; https://doi.org/10.3390/drones8100523 - 26 Sep 2024

Abstract

To address the challenge of low detection accuracy and slow detection speed in unmanned aerial vehicle (UAV) aerial images target detection tasks, caused by factors such as complex ground environments, varying UAV flight altitudes and angles, and changes in lighting conditions, this study [...] Read more.

To address the challenge of low detection accuracy and slow detection speed in unmanned aerial vehicle (UAV) aerial images target detection tasks, caused by factors such as complex ground environments, varying UAV flight altitudes and angles, and changes in lighting conditions, this study proposes an end-to-end adaptive multi-scale feature extraction and fusion detection network, named AMFEF-DETR. Specifically, to extract target features from complex backgrounds more accurately, we propose an adaptive backbone network, FADC-ResNet, which dynamically adjusts dilation rates and performs adaptive frequency awareness. This enables the convolutional kernels to effectively adapt to varying scales of ground targets, capturing more details while expanding the receptive field. We also propose a HiLo attention-based intra-scale feature interaction (HLIFI) module to handle high-level features from the backbone. This module uses dual-pathway encoding of high and low frequencies to enhance the focus on the details of dense small targets while reducing noise interference. Additionally, the bidirectional adaptive feature pyramid network (BAFPN) is proposed for cross-scale feature fusion, integrating semantic information and enhancing adaptability. The Inner-Shape-IoU loss function, designed to focus on bounding box shapes and incorporate auxiliary boxes, is introduced to accelerate convergence and improve regression accuracy. When evaluated on the VisDrone dataset, the AMFEF-DETR demonstrated improvements of 4.02% and 16.71% in mAP50 and FPS, respectively, compared to the RT-DETR. Additionally, the AMFEF-DETR model exhibited strong robustness, achieving mAP50 values 2.68% and 3.75% higher than the RT-DETR and YOLOv10, respectively, on the HIT-UAV dataset. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in UAV Technology)

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22 pages, 5586 KiB

Open AccessArticle

Vegetation Type Preferences in Red Deer (Cervus elaphus) Determined by Object Detection Models

by Annika Fugl, Lasse Lange Jensen, Andreas Hein Korsgaard, Cino Pertoldi and Sussie Pagh

Drones 2024, 8(10), 522; https://doi.org/10.3390/drones8100522 - 26 Sep 2024

Abstract

This study investigates the possibility of utilising a drone equipped with a thermal camera to monitor the spatial distribution of red deer (Cervus elaphus) and to determine their behavioural patterns, as well as preferences for vegetation types in a moor in [...] Read more.

This study investigates the possibility of utilising a drone equipped with a thermal camera to monitor the spatial distribution of red deer (Cervus elaphus) and to determine their behavioural patterns, as well as preferences for vegetation types in a moor in Denmark. The spatial distribution of red deer was mapped according to time of day and vegetation types. Reed deer were separated manually from fallow deer (Dama dama) due to varying footage quality. Automated object detection from thermal camera footage was used to identification of two behaviours, “Eating” and “Lying”, enabling insights into the behavioural patterns of red deer in different vegetation types. The results showed a migration of red deer from the moors to agricultural fields during the night. The higher proportion of time spent eating in agricultural grass fields compared to two natural vegetation types, “Grey dune” and “Decalcified fixed dune”, indicates that fields are important foraging habitats for red deer. The red deer populations were observed significantly later on grass fields compared to the natural vegetation types. This may be due to human disturbance or lack of randomisation of the flight time with the drone. Further studies are suggested across different seasons as well as the time of day for a better understanding of the annual and diurnal foraging patterns of red deer. Full article

(This article belongs to the Special Issue Drone Advances in Wildlife Research: 2nd Edition)

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21 pages, 8785 KiB

Open AccessArticle

Enhancing Unmanned Aerial Vehicle Path Planning in Multi-Agent Reinforcement Learning through Adaptive Dimensionality Reduction

by Haotian Shi, Zilin Zhao, Jiale Chen, Mengjie Zhou and Yang Liu

Drones 2024, 8(10), 521; https://doi.org/10.3390/drones8100521 - 25 Sep 2024

Viewed by 301

Abstract

Unmanned Aerial Vehicles (UAVs) have become increasingly important in various applications, including environmental monitoring, disaster response, and surveillance, due to their flexibility, efficiency, and ability to access hard-to-reach areas. Effective path planning for multiple UAVs exploring a target area is crucial for maximizing [...] Read more.

Unmanned Aerial Vehicles (UAVs) have become increasingly important in various applications, including environmental monitoring, disaster response, and surveillance, due to their flexibility, efficiency, and ability to access hard-to-reach areas. Effective path planning for multiple UAVs exploring a target area is crucial for maximizing coverage and operational efficiency. This study presents a novel approach to optimizing collaborative navigation for UAVs using multi-agent reinforcement learning (MARL). To enhance the efficiency of this process, we introduce the Adaptive Dimensionality Reduction (ADR) framework, which includes Autoencoders (AEs) and Principal Component Analysis (PCA) for dimensionality reduction and feature extraction. The ADR framework significantly reduces computational complexity by simplifying high-dimensional state spaces while preserving crucial information. Additionally, we incorporate communication modules to facilitate inter-UAV coordination, further improving path planning efficiency. Our experimental results demonstrate that the proposed approach significantly enhances exploration performance and reduces computational complexity, showcasing the potential of combining MARL with ADR techniques for advanced UAV navigation in complex environments. Full article

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28 pages, 7604 KiB

Open AccessArticle

Design and Implementation of a Novel UAV-Assisted LoRaWAN Network

by Honggang Zhao, Wenxin Tang, Sitong Chen, Aoyang Li, Yong Li and Wei Cheng

Drones 2024, 8(10), 520; https://doi.org/10.3390/drones8100520 - 25 Sep 2024

Viewed by 228

Abstract

When LoRaWAN networks are deployed in complex environments with buildings, jungles, and other obstacles, the communication range of LoRa signals experiences a notable reduction, primarily due to multipath propagation, fading, and interference. With the flight advantage of height, mobility, and flexibility, UAV can [...] Read more.

When LoRaWAN networks are deployed in complex environments with buildings, jungles, and other obstacles, the communication range of LoRa signals experiences a notable reduction, primarily due to multipath propagation, fading, and interference. With the flight advantage of height, mobility, and flexibility, UAV can provide line-of-sight (LOS) communication or more reliable communication in many scenarios, which can be used to enhance the LoRaWAN network’s performance. In this paper, a novel UAV-assisted LoRaWAN network is designed and implemented. Specifically, a UAV-assisted LoRaWAN network system architecture is proposed to improve the LoRaWAN network coverage and communication reliability, in which the UAV architecture of “UAV + Remote Controller + Server” is combined with the traditional LoRaWAN architecture of “End-Device + Gateway + Server”. Then, the implementation of the UAV gateway and the remote controller relay is presented, which play the important role of forwarding LoRaWAN frames transparently in our proposed architecture. In detail, the UAV gateway is developed based on the UAV’s PSDK and classical LoRa packet forwarder, and the remote controller relay is developed based on UAV’s MSDK. The experimental results show that the network coverage and communication reliability of our proposed LoRaWAN network have been significantly improved, effectively supporting a wide range of LoRaWAN applications. Specifically, when the end-device is deployed 1.3 km away with numerous obstacles in the propagation environment, with the UAV altitude advantage and the remote controller’s relay capability, the proposed system achieved an SNR of 5 db and an RSSI of −80 dbm with a packet loss rate of 3%. In comparison, the ground gateway only achieved an SNR of −16 db and an RSSI of −113 dbm with a packet loss rate of 73%. Full article

(This article belongs to the Section Drone Communications)

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31 pages, 3998 KiB

Open AccessArticle

Delivery Route Scheduling of Heterogeneous Robotic System with Customers Satisfaction by Using Multi-Objective Artificial Bee Colony Algorithm

by Zhihuan Chen, Shangxuan Hou, Zuao Wang, Yang Chen, Mian Hu and Rana Muhammad Adnan Ikram

Drones 2024, 8(10), 519; https://doi.org/10.3390/drones8100519 - 24 Sep 2024

Viewed by 306

Abstract

This study addresses the route scheduling problem for the heterogeneous robotic delivery system (HRDS) that perform delivery tasks in an urban environment. The HRDS comprises two distinct types of vehicles: an unmanned ground vehicle (UGV), which is constrained by road networks, and an [...] Read more.

This study addresses the route scheduling problem for the heterogeneous robotic delivery system (HRDS) that perform delivery tasks in an urban environment. The HRDS comprises two distinct types of vehicles: an unmanned ground vehicle (UGV), which is constrained by road networks, and an unmanned aerial vehicle (UAV), which is capable of traversing terrain but has limitations in terms of energy and payload. The problem is formulated as an optimal route scheduling problem in a road network, where the goal is to find the route with minimum delivery cost and maximum customer satisfaction (CS) enabling the UAV to deliver packages to customers. We propose a new method of route scheduling based on an improved artificial bee colony algorithm (ABC) and the non-dominated sorting genetic algorithm II (NSGA-II) that provides the optimal delivery route. The effectiveness and superiority of the method we proposed are demonstrated by comparison in simulations. Moreover, the physical experiments further validate the practicality of the model and method. Full article

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Drones, Volume 8, Issue 10 (October 2024) – 9 articles

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