Autonomous Unmanned Aerial Vehicles Research Articles

Collision Avoidance in Circular Motion of a Fixed-Wing Drone Formation Based on Rotational Modification of Artificial Potential Field

In coordinated circular motion of a group of autonomous unmanned aerial vehicles (UAVs or drones), it is important to ensure that collisions between them are avoided. A typical situation occurs when one of the drones in a circular formation needs to overtake the drone ahead. The reason for such an overtake may be due to a given geometry of the UAV formation, when this configuration of a given relative position of the drones has changed for some reason. In this case, the limited maneuverability of UAVs of exactly fixed-wing type requires taking into account the peculiarities of their dynamics in the synthesis of the collision avoidance algorithm. The impossibility of the airspeed for a fixed-wing type UAV to drop below a certain minimum value also plays a role here. In this paper, we propose to use an approach based on vortex vector fields, which are essentially a rotational modification of the artificial potential field (APF) method. In this case, the path following algorithm developed in our previous works provides the circular motion. As a result, a collision avoidance algorithm has been developed that works efficiently by maintaining a coordinated circular motion of the autonomous drone formation without unnecessary turns. The proposed algorithm was named Artificial Potential Field for Circular Motion (abbreviated as APFfCM). Using the direct Lyapunov method, it is shown that the trajectories of the formation system have uniform boundedness (UB) when using the proposed control algorithm. Due to the boundedness of the candidate Lyapunov function, it is guaranteed that no collision event between drones will occur. Thus the control objective of providing coordinated circular motion for an autonomous fixed-wing type drone formation without collisions is achieved. Fixed-wing (“flying wing”) UAV models in MATLAB/Simulink environment demonstrate the effective performance of the proposed algorithm. These models have both full nonlinear dynamics and implementation of tuned autopilots stabilizing angular and trajectory motion.

Safety Inspections and Gas Monitoring in Hazardous Mining Areas Shortly After Blasting Using Autonomous UAVs

Safety Inspections and Gas Monitoring in Hazardous Mining Areas Shortly After Blasting Using Autonomous UAVs

A vision-inertial interaction-based autonomous UAV positioning algorithm

A vision-inertial interaction-based autonomous UAV positioning algorithm

Coordinated Control of a Tethered Autonomous Docking UAV

Coordinated Control of a Tethered Autonomous Docking UAV

Resilient Multi-Hop Autonomous UAV Networks With Extended Lifetime for Multi-Target Surveillance

Resilient Multi-Hop Autonomous UAV Networks With Extended Lifetime for Multi-Target Surveillance

Vision-Based Deep Reinforcement Learning of Unmanned Aerial Vehicle (UAV) Autonomous Navigation Using Privileged Information

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.

Comparative Analysis of Deep Reinforcement Learning Algorithms for Hover-to-Cruise Transition Maneuvers of a Tilt-Rotor Unmanned Aerial Vehicle

Work on trajectory optimization is evolving rapidly due to the introduction of Artificial-Intelligence (AI)-based algorithms. Small UAVs are expected to execute versatile maneuvers in unknown environments. Prior studies on these UAVs have focused on conventional controller design, modeling, and performance, which have posed various challenges. However, a less explored area is the usage of reinforcement-learning algorithms for performing agile maneuvers like transition from hover to cruise. This paper introduces a unified framework for the development and optimization of a tilt-rotor tricopter UAV capable of performing Vertical Takeoff and Landing (VTOL) and efficient hover-to-cruise transitions. The UAV is equipped with a reinforcement-learning-based control system, specifically utilizing algorithms such as Deep Deterministic Policy Gradient (DDPG), Trust Region Policy Optimization (TRPO), and Proximal Policy Optimization (PPO). Through extensive simulations, the study identifies PPO as the most robust algorithm, achieving superior performance in terms of stability and convergence compared with DDPG and TRPO. The findings demonstrate the efficacy of DRL in leveraging the unique dynamics of tilt-rotor UAVs and show a significant improvement in maneuvering precision and control adaptability. This study demonstrates the potential of reinforcement-learning algorithms in advancing autonomous UAV operations by bridging the gap between dynamic modeling and intelligent control strategies, underscoring the practical benefits of DRL in aerial robotics.

Development, Applications, and Future Trends of UAV Autonomous Navigation Technology

Background and Significance: With the rapid advancement of drone technology, automatic navigation systems have become one of the most critical components, playing an increasingly vital role in modern technological applications. As drones become more integral to various industries, the need for sophisticated navigation systems that can operate autonomously and efficiently has grown exponentially. These systems not only enhance the operational capabilities of unmanned aerial vehicles (UAVs) but also expand their potential uses across diverse fields such as agriculture, logistics, defense, and environmental monitoring. Understanding the development and current state of UAV automatic navigation technology is crucial for anticipating future trends and guiding innovation in this rapidly evolving field. Contribution of This Study: This article offers a comprehensive exploration of the evolution of UAV automatic navigation technology, providing a detailed analysis of its current applications across different sectors. By evaluating existing technologies and their performance, the study highlights the strengths and limitations of current systems, offering insights that can drive future advancements. Additionally, the article predicts and discusses potential future trends in UAV navigation technology, aiming to contribute to the ongoing innovation and broader adoption of these systems. This work serves as a valuable reference for researchers and practitioners looking to advance UAV automatic navigation technology and explore new application possibilities.

Research on UAV Autonomous Recognition and Approach Method for Linear Target Splicing Sleeves Based on Deep Learning and Active Stereo Vision

This study proposes an autonomous recognition and approach method for unmanned aerial vehicles (UAVs) targeting linear splicing sleeves. By integrating deep learning and active stereo vision, this method addresses the navigation challenges faced by UAVs during the identification, localization, and docking of splicing sleeves on overhead power transmission lines. First, a two-stage localization strategy, LC (Local Clustering)-RB (Reparameterization Block)-YOLO (You Only Look Once)v8n (OBB (Oriented Bounding Box)), is developed for linear target splicing sleeves. This strategy ensures rapid, accurate, and reliable recognition and localization while generating precise waypoints for UAV docking with splicing sleeves. Next, virtual reality technology is utilized to expand the splicing sleeve dataset, creating the DSS dataset tailored to diverse scenarios. This enhancement improves the robustness and generalization capability of the recognition model. Finally, a UAV approach splicing sleeve (UAV-ASS) visual navigation simulation platform is developed using the Robot Operating System (ROS), the PX4 open-source flight control system, and the GAZEBO 3D robotics simulator. This platform simulates the UAV’s final approach to the splicing sleeves. Experimental results demonstrate that, on the DSS dataset, the RB-YOLOv8n(OBB) model achieves a mean average precision (mAP0.5) of 96.4%, with an image inference speed of 86.41 frames per second. By incorporating the LC-based fine localization method, the five rotational bounding box parameters (x, y, w, h, and angle) of the splicing sleeve achieve a mean relative error (MRE) ranging from 3.39% to 4.21%. Additionally, the correlation coefficients (ρ) with manually annotated positions improve to 0.99, 0.99, 0.98, 0.95, and 0.98, respectively. These improvements significantly enhance the accuracy and stability of splicing sleeve localization. Moreover, the developed UAV-ASS visual navigation simulation platform effectively validates high-risk algorithms for UAV autonomous recognition and docking with splicing sleeves on power transmission lines, reducing testing costs and associated safety risks.

Determination of Material Volume Using UAVs in Land Surface Change: The Case of Konuralp Campus (Düzce)

In this study examined the availability of UAV in determining material volume in excavation and fill works. The study aims on calculating the volume of an excavation work areas 1841 m² at the parking lot of the Faculty of Sports Sciences at Düzce University Konuralp Campus and a filling work areas 2759 m² on the access road to the Environmental and Health Specialization Laboratory. Autonomous UAV flights were performed before and after the excavation and filling works in the study areas. Besides, following these UAV flights, terrestrial measurements were made in the same way. The study was founded that UAV data analysis results an excavation volume of 7121 m³ in the parking area and a filling volume of 5752 m³ on the road. According to calculations used terrestrial measurement methods, the excavation volume in the parking area was 7007 m³ and the filling volume on the road was 5683 m³. An average difference of 1.42% was found between UAV data and terrestrial measurement methods. The results were showed the usability of UAV systems in detected material volume.

3D path planning for UAV based on A hybrid algorithm of marine predators algorithm with quasi-oppositional learning and differential evolution

3D path planning is a critical requirement for the autonomy of unmanned aerial vehicle (UAV) navigation systems. In this paper, we propose a novel approach, the differential evolution combined marine predators algorithm (DECMPA), specifically tailored to address the UAV 3D path planning problem in complex scenarios. To handle stringent constraints, DECMPA adopts a multi-step approach. Initially, it utilizes quasi-oppositional learning to generate a more uniformly distributed initial population. Throughout the iterative process, it probabilistically generates quasi-oppositional populations and merges them with existing populations, selecting the superior individuals for the next generation to mitigate local optima. Additionally, DECMPA integrates the variance, crossover, and selection processes of differential evolution into the marine predators algorithm iterations. The greedy selection of test and target vectors further accelerates population convergence. Numerical optimization experiments are conducted using the 10 and 20-dimensional CEC 2021 test suite. Compared to other algorithms, DECMPA exhibits superior solution accuracy and convergence speed, resulting in substantial performance enhancement, thus establishing itself as an efficient algorithm. Furthermore, the adoption of spherical vector coordinates in solution encoding ensures higher quality and facilitates the production of feasible solutions. To verify the effectiveness and practicality of the proposed algorithm, comprehensive path planning simulation experiments are conducted. DECMPA is compared against nine other state-of-the-art heuristic algorithms across six 3D scenarios of varying complexity. The experimental results demonstrate DECMPA’s superiority in terms of optimal cost acquisition, solution quality, and stability. In conclusion, DECMPA presents a promising solution for addressing the challenges of UAV 3D path planning in complex environments. Its innovative approach and superior performance underscore its potential for real-world application in autonomous UAV navigation systems.

AI-based autonomous UAV swarm system for weed detection and treatment: Enhancing organic orange orchard efficiency with agriculture 5.0

Weeds significantly threaten agricultural productivity by competing with crops for nutrients, particularly in organic farming, where chemical herbicides are prohibited. On Spain’s Mediterranean coast, organic citrus farms face increasing challenges from invasive species like Araujia sericifera and Cortaderia selloana, which further complicate cover crop management. This study introduces a swarm system of unmanned aerial vehicles (UAVs) equipped with neural networks based on YOLOv10 for the detection and geo-location of these invasive weeds. The system achieves F1-scores of 0.78 for Araujia sericifera and 0.80 for Cortaderia selloana. Using GPS and RTK, the UAVs generate KML files to guide diffuser drones for precise, localized treatments with organic products. By automating the detection, treatment, and elimination of invasive species, the system enhances both productivity and sustainability in organic farming. Additionally, the proposed solution addresses the high labor costs associated with manual weeding by reducing the need for human intervention. A comprehensive economic analysis indicates potential savings ranging from 1810 to 2650 € per hectare, depending on farm size. This innovative approach not only improves weed control efficiency but also promotes environmental sustainability, offering a scalable solution for organic and conventional agriculture alike.

Autonomous UAV Chasing with Monocular Vision: A Learning-Based Approach

In recent years, unmanned aerial vehicles (UAVs) have shown significant potential across diverse applications, drawing attention from both academia and industry. In specific scenarios, UAVs are expected to achieve formation flying without relying on communication or external assistance. In this context, our work focuses on the classic leader-follower formation and presents a learning-based UAV chasing control method that enables a quadrotor UAV to autonomously chase a highly maneuverable fixed-wing UAV. The proposed method utilizes a neural network called Vision Follow Net (VFNet), which integrates monocular visual data with the UAV’s flight state information. Utilizing a multi-head self-attention mechanism, VFNet aggregates data over a time window to predict the waypoints for the chasing flight. The quadrotor’s yaw angle is controlled by calculating the line-of-sight (LOS) angle to the target, ensuring that the target remains within the onboard camera’s field of view during the flight. A simulation flight system is developed and used for neural network training and validation. Experimental results indicate that the quadrotor maintains stable chasing performance through various maneuvers of the fixed-wing UAV and can sustain formation over long durations. Our research explores the use of end-to-end neural networks for UAV formation flying, spanning from perception to control.

Research on Autonomous Navigation of Unmanned Aerial Vehicles Based on Correlation-Based Image Comparison Methods

Relevance. Autonomous navigation of unmanned aerial vehicles (UAVs) is one of the key challenges in the modern aerospace industry. Specifically, for small UAVs, the task of autonomous navigation becomes even more complex due to limitations in computational resources and sensor capabilities. Optimizing navigation methods under such conditions is a pressing issue that requires solutions capable of providing high performance with minimal resource consumption.Objective. Improving the efficiency of autonomous navigation for small UAVs by using correlation methods for image comparison. Achieving this objective is linked to the development and evaluation of algorithms that provide high-speed and accurate navigation with limited computational resources. The work uses methods such as the autocorrelation function, Pearson correlation, the structural similarity index, and a simple neural network for image comparison.Solution. The research showed that the autocorrelation-based approach demonstrates the best performance under low computational resources. It ensures high-speed processing of the entire image and shows optimal detection accuracy. Compared to other methods presented in the study, the autocorrelation approach is capable of working not only with noise in the "reference" map but also of using alternative areas with altered patterns as detected regions.The scientific novelty of the study is determined by the systematic comparison of various methods applied to the task of image comparison for small UAVs with limited computational resources. Unlike well-known works in the field of correlation-extreme systems, this research focuses on the use of a "reference map" and a search area, representing two different images of the same terrain taken from different sources. This is a key difference, as most methods are not highly efficient in processing such images where patterns may differ significantly.The practical significance of the developed algorithm lies in the fact that the proposed autocorrelation-based method can be used by developers of autonomous small UAV control systems to reduce computational load and increase data processing speed.

Learning Optimizer-Based Visual Analytics Method to Detect Targets in Autonomous Unmanned Aerial Vehicles

Visual analytics is vital for identifying targets by differentiating their structure and texture from unmanned aerial vehicles (UAVs). Object sensing disturbance and swift texture differentiation are tedious due to the UAV displacements. For improving the accuracy of target detection, this article introduces a learning optimizer-based visual analytics method. The proposed method assimilates deep learning and a gradient descent algorithm for feature differentiation and error minimization concurrently. The captured images are identified using multiple structural feature variations and are correlated with similar stored images. The features are extracted at different displacement and structural changing instances for leveraging accuracy. The learning process trains the similarity features during different differentiation factors. In the feature extraction, the minimum slope points are identified using a gradient descent algorithm by assigning random weights. As the differentiation increases using similar features, the minimum similarity value is detection. Postdetection, the weights are incremental and linear across different feature slopes. Therefore, the accuracy increases under varying displacement instances, preventing target misdetection. The gradient function is invariable between the minimum and maximum values for identifying high-precision features. This ensures optimal detection of different buildings and structures with high accuracy.

Integrated Learning-Based Framework for Autonomous Quadrotor UAV Landing on a Collaborative Moving UGV

Integrated Learning-Based Framework for Autonomous Quadrotor UAV Landing on a Collaborative Moving UGV

Joint Design of Transmit Waveform and Altitude for Unmanned Aerial Vehicle-Enabled Integrated Sensing and Wireless Power Transfer Systems

Recently, unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) has received great attention as a promising technology for providing stable power to energy-constrained devices by navigating three-dimensional (3D) space, particularly in challenging environments such as maritime networks and smart cities. Additionally, UAV-enabled radar sensing has gained significant attention as a key technology for future 6G networks, as it enables high-accuracy sensing for various applications, such as target detection and tracking, surveillance, and environmental monitoring, as well as autonomous UAV operation. In this regard, we investigated UAV-enabled integrated sensing and wireless power transfer (ISWPT) systems that combine radar sensing and WPT operations on a unified hardware platform, sharing the same spectrum of resources. In order to accurately sense multiple targets and efficiently transfer power to multiple devices at the same time, we propose a method for jointly designing the transmit waveform and UAV altitude, taking into account the fundamental trade-off between radar sensing performance with the desired beam pattern and WPT performance with the desired harvested power of the devices. We first developed an effective method to obtain the optimal waveform and altitude by solving a challenging non-convex optimization problem. Based on this, we developed another efficient, low-complexity method by exploring a novel transmit waveform and optimizing its parameters to reduce computational complexity and thereby lower power consumption in UAVs. The numerical results verify that the proposed method significantly improves both radar sensing and WPT performance, as well as substantially reduces computational complexity.

Retraction Note: An agent architecture for autonomous UAV flight control in object classification and recognition missions

Retraction Note: An agent architecture for autonomous UAV flight control in object classification and recognition missions

Multi-UAV Escape Target Search: A Multi-Agent Reinforcement Learning Method.

The multi-UAV target search problem is crucial in the field of autonomous Unmanned Aerial Vehicle (UAV) decision-making. The algorithm design of Multi-Agent Reinforcement Learning (MARL) methods has become integral to research on multi-UAV target search owing to its adaptability to the rapid online decision-making required by UAVs in complex, uncertain environments. In non-cooperative target search scenarios, targets may have the ability to escape. Target probability maps are used in many studies to characterize the likelihood of a target's existence, guiding the UAV to efficiently explore the task area and locate the target more quickly. However, the escape behavior of the target causes the target probability map to deviate from the actual target's position, thereby reducing its effectiveness in measuring the target's probability of existence and diminishing the efficiency of the UAV search. This paper investigates the multi-UAV target search problem in scenarios involving static obstacles and dynamic escape targets, modeling the problem within the framework of decentralized partially observable Markov decision process. Based on this model, a spatio-temporal efficient exploration network and a global convolutional local ascent mechanism are proposed. Subsequently, we introduce a multi-UAV Escape Target Search algorithm based on MAPPO (ETS-MAPPO) for addressing the escape target search difficulty problem. Simulation results demonstrate that the ETS-MAPPO algorithm outperforms five classic MARL algorithms in terms of the number of target searches, area coverage rate, and other metrics.

Optimal path planning for unmanned aerial vehicles in power line inspection in rechargeable scenario

To address the issue that a single charge of UAVs (Unmanned Aerial Vehicles) do not satisfy task requirements, this paper proposes a solution involving the deployment of charging stations in the mission area. An energy-efficient path planning method is designed for UAVs with charging stations, which simultaneously determines the deployment locations and quantities of charging stations. The method formulates the energy optimization path planning problem of UAVs as an integer linear programming and an algorithm based on greedy strategy is designed for solving the model. A numerical example including simulations across various scenarios demonstrate that the proposed method can search a more energy-efficient paths compared to single-agent multi-sortie schemes with a greedy strategy. This research contributes to the development of intelligent and autonomous UAV systems for power line inspection, enhancing grid reliability and safety.