Algorithm For Unmanned Aerial Vehicle Research Articles

SOD-YOLO: A lightweight small object detection framework

Currently, lightweight small object detection algorithms for unmanned aerial vehicles (UAVs) often employ group convolutions, resulting in high Memory Access Cost (MAC) and rendering them unsuitable for edge devices that rely on parallel computing. To address this issue, we propose the SOD-YOLO model based on YOLOv7, which incorporates a DSDM-LFIM backbone network and includes a small object detection branch. The DSDM-LFIM backbone network, which combines Deep-Shallow Downsampling Modules (DSD Modules) and Lightweight Feature Integration Modules (LFI Modules), avoids excessive use of group convolutions and element-wise operations. The DSD Module focuses on extracting both deep and shallow features from feature maps using fewer parameters to obtain richer feature representations. The LFI Module, is a dual-branch feature integration module designed to consolidate feature information. Experimental results demonstrate that the SOD-YOLO model achieves an AP50 of 50.7% and a FPS of 72.5 on the VisDrone validation set. Compared to YOLOv7, our model reduces computational costs by 20.25% and decreases the number of parameters by 17.89%. After scaling the number of channels in the model, it achieves an AP50 of 33.4% with an inference time of 27.3ms on the Atlas 200I DK A2. These experimental results indicate that the SOD-YOLO model can effectively perform small object detection tasks in a large number of aerial images captured by UAVs.

Dynamic environment UAV deployment algorithm based on potential game theory

AbstractTo address the issue of low coverage resulting from the challenge of acquiring the optimal deployment position in commonly used distributed deployment algorithms, this study presents a three‐dimensional deployment algorithm for Unmanned Aerial Vehicles (UAVs) based on potential games. First, a local mutually beneficial game model is designed to demonstrate the existence of exact potential games and Nash equilibrium. The Nash equilibrium solution corresponds to the maximum coverage. Next, drawing inspiration from exploration, a solution method called Exploration Spatial Adaptive Play is proposed. It utilizes the maximum utility function value from multiple step sizes in the exploration direction to update the action selection probability, thereby ensuring the optimal deployment position in each decision cycle. To address the issue of sensor position error, a method for processing sensor position errors is proposed. The simulation results demonstrate that the proposed distributed deployment algorithm achieves higher coverage compared to commonly used methods.

USSC-YOLO: Enhanced Multi-Scale Road Crack Object Detection Algorithm for UAV Image.

Road crack detection is of paramount importance for ensuring vehicular traffic safety, and implementing traditional detection methods for cracks inevitably impedes the optimal functioning of traffic. In light of the above, we propose a USSC-YOLO-based target detection algorithm for unmanned aerial vehicle (UAV) road cracks based on machine vision. The algorithm aims to achieve the high-precision detection of road cracks at all scale levels. Compared with the original YOLOv5s, the main improvements to USSC-YOLO are the ShuffleNet V2 block, the coordinate attention (CA) mechanism, and the Swin Transformer. First, to address the problem of large network computational spending, we replace the backbone network of YOLOv5s with ShuffleNet V2 blocks, reducing computational overhead significantly. Next, to reduce the problems caused by the complex background interference, we introduce the CA attention mechanism into the backbone network, which reduces the missed and false detection rate. Finally, we integrate the Swin Transformer block at the end of the neck to enhance the detection accuracy for small target cracks. Experimental results on our self-constructed UAV near-far scene road crack i(UNFSRCI) dataset demonstrate that our model reduces the giga floating-point operations per second (GFLOPs) compared to YOLOv5s while achieving a 6.3% increase in mAP@50 and a 12% improvement in mAP@ [50:95]. This indicates that the model remains lightweight meanwhile providing excellent detection performance. In future work, we will assess road safety conditions based on these detection results to prioritize maintenance sequences for crack targets and facilitate further intelligent management.

Algorithms for Route Planning and Navigation of Unmanned Aerial Vehicles

A brief overview of the results of recent research published in open sources in the field of route planning and navigation algorithms for unmanned aerial vehicles (UAV) is presented. Works devoted to global and local planning of trajectories taking into account known and detected obstacles in flight, as well as issues of navigation of groups of drones, are considered. Various approaches are analyzed, including graph algorithms (A*, Dijkstra, Rapidly-exploring Random Trees), methods of data mining in real time, potential fields. Special attention is paid to work on the use of neural networks and machine learning, SLAM and multi-agent technologies for planning UAV routes. The advantages and disadvantages of the main groups of algorithms are considered. A conclusion is drawn about the prospects for using hybrid methods, as well as machine learning technologies, to build intelligent UAV traffic control systems.

Vertical Switching Algorithm for Unmanned Aerial Vehicle in Power Grid Heterogeneous Communication Networks

The rapid development of wireless network technology has led to the coexistence of various heterogeneous wireless networks (HWNs). To ensure that users enjoy normal and diversified services, research on vertical switching technology has become an inevitable trend. However, most current vertical switching algorithms only consider static situations or single services, which are not suitable for power grid scenarios. This paper studies the vertical switching problem of wireless heterogeneous networks for unmanned aerial vehicles (UAVs) performing inspection tasks in power grid scenarios. In this model, a UAV for power grid inspection needs to plan its flight trajectory, avoid obstacles, and find the optimal trajectory to reach each inspection point. Throughout the UAV inspection process, we must ensure the quality of communication services for the UAV. The UAV dynamically selects different networks for access at different locations, presenting a dynamic network selection and vertical switching problem. This paper proposes a method that combines trajectory planning and network selection, which first utilizes the A-star algorithm to obtain suitable trajectories, and then evaluates and judges networks based on the Fuzzy Analytic Hierarchy Process (FAHP) to determine the most appropriate network. It is worth noting that this paper considers three service requirements and seven network attributes under three types of heterogeneous wireless networks. Numerical results show that this method can better meet the requirements of UAV inspection tasks and reduce the number of switches, thus addressing the issue of terminal vertical switches in power grid scenarios.

HeMoDU: High-Efficiency Multi-Object Detection Algorithm for Unmanned Aerial Vehicles on Urban Roads.

Unmanned aerial vehicle (UAV)-based object detection methods are widely used in traffic detection due to their high flexibility and extensive coverage. In recent years, with the increasing complexity of the urban road environment, UAV object detection algorithms based on deep learning have gradually become a research hotspot. However, how to further improve algorithmic efficiency in response to the numerous and rapidly changing road elements, and thus achieve high-speed and accurate road object detection, remains a challenging issue. Given this context, this paper proposes the high-efficiency multi-object detection algorithm for UAVs (HeMoDU). HeMoDU reconstructs a state-of-the-art, deep-learning-based object detection model and optimizes several aspects to improve computational efficiency and detection accuracy. To validate the performance of HeMoDU in urban road environments, this paper uses the public urban road datasets VisDrone2019 and UA-DETRAC for evaluation. The experimental results show that the HeMoDU model effectively improves the speed and accuracy of UAV object detection.

Multiple Task Assignment Algorithms for Unmanned Aerial Vehicles

To fulfill the requirement of assigning task the auction algorithm is widely used. There are many classical auction algorithms those performances not up to the mark while dealing with multi-UAVs dynamic task assignment. SWARM UAVs are made up of a large number of small UAVs with limited mission resources that can operate in an autonomous, appropriate and universal manner. Based on the in-depth research of the traditional auction algorithm CAA, this paper proposes an iterative method that can improve the task allocation efficiency of multi-UAV, namely the two-stage auction algorithm. At the same time, in order to improve the daily management of airborne computing and communication resources of UAV, this paper overcomes the difficulties caused by data coupling between task allocation and path planning, and proposes a decentralized task allocation algorithm, that is, UAV re-checks the unreasonable task allocation results within the task allocation cycle. This method has the advantages of algorithm security and unpredictability, and it can control the error of task assignment evaluation within a specific range through finite complexity calculation. Simulation results show that the algorithm is effective in computing efficiency and task execution efficiency.

Three-dimensional path planning with enhanced gravitational search algorithm for unmanned aerial vehicle

Abstract Path planning for the unmanned aerial vehicle (UAV) is to assist in finding the proper path, serving as a critical role in the intelligence of a UAV. In this paper, a path planning for UAV in three-dimensional environment (3D) based on enhanced gravitational search algorithm (EGSA) is put forward, taking the path length, yaw angle, pitch angle, and flight altitude as considerations of the path. Considering EGSA is easy to fall into local optimum and convergence insufficiency, two factors that are the memory of current optimal and random disturbance with chaotic levy flight are adopted during the update of particle velocity, improving the balance between exploration and exploitation for EGSA through different time-varying characteristics. With the identical cost function, EGSA is compared with seven peer algorithms, such as moth flame optimization algorithm, gravitational search algorithm, and five variants of gravitational search algorithm. The experimental results demonstrate that EGSA is superior to the seven comparison algorithms on CEC 2020 benchmark functions and the path planning method based on EGSA is more valuable than the other seven methods in diverse environments.

Analysis and optimization of path finding algorithm for unmanned aerial vehicles

Analysis and optimization of path finding algorithm for unmanned aerial vehicles

Research on adaptive shooting algorithm for unmanned aerial vehicles under typical operating conditions of overhead transmission lines

We consider typical environmental conditions, such as different shooting backgrounds, lighting, and shooting distances, and study adaptive shooting algorithms for unmanned aerial vehicle (UAV) on-site environments under typical working conditions to achieve high-quality acquisition of UAV inspection images and reduce the technical difficulty of artificial intelligence recognition of inspection images. Based on the collected high-quality images and the characteristics of unmanned aerial vehicle inspection images, a deep learning technology framework is adopted to study the intelligent identification method of hidden dangers in transmission line equipment and channel environment, achieving the intelligent recognition of pin level fine particle defects.

A comparative analysis of path planning algorithms for unmanned aerial vehicles

In terms of trajectory planning, the main objective is to reach a certain location in the shortest time possible, avoiding obstacles along the way. This paper proposes two path planning methods, in an environment with presence of obstacles, Probabilistic RoadMap and a fusion between Voronoi diagram and Dijkstra's Algorithm. Path planning for both algorithms is developed in the XY coordinate plane, therefore only horizontal movement is considered. This means that the UAV must fly around obstacles and cannot fly over or under them. The use of two-dimensional space in favour of three-dimensional space reduces the implementation time of the algorithm for solving the problem. Finally, an analysis of the length and the computational time of the trajectories generated by the proposed algorithms is made.

A precise localization algorithm for unmanned aerial vehicles integrating visual-internal odometry and cartographer

Accurate positioning in space is an important foundation for ensuring the stability of autonomous flight and successful mapping and navigation of unmanned aerial vehicles (UAVs). At present, the SLAM algorithm based on the Cartographer algorithm for real-time positioning and mapping is widely used in fields such as robot navigation and autonomous driving. However, in the context of UAV applications, this algorithm has a high dependence on the amount of point cloud information in the surrounding environment, and cannot achieve precise positioning in open spaces with insufficient lighting and fewer feature points, resulting in significant mapping errors. In order to solve the problem of low positioning estimation accuracy in the Cartographer algorithm in above environment, this paper proposes a precise positioning algorithm for UAVs that integrates VIO and Cartographer. This algorithm can continuously output more accurate position estimation information during UAV flight, compensating for the problem of inaccurate position estimation caused by partial feature loss or coordinate system drift in point clouds. In addition, this algorithm ensures navigation obstacle avoidance in narrow spaces by improving positioning accuracy and mapping accuracy, making it more applicable in the field of UAVs. Finally, the effectiveness of the proposed positioning algorithm was verified through experimental analysis of the Cartographer dataset and practical testing of UAVs in real scenarios.

A self-adaptive parallel image stitching algorithm for unmanned aerial vehicles in edge computing environments

ABSTRACT The overlap between edge computing and unmanned aerial systems enables Unmanned Aerial Vehicles (UAV) to quickly offload image processing tasks onto edge devices, avoiding the transmission of images over long distances. To improve the speed and efficiency of UAV image stitching in an edge computing environment, this paper proposes an adaptive UAV image parallel stitching algorithm in an edge computing environment. The algorithm incorporates both route-based parallel processing and inverted binary tree-based parallel processing, dividing the image stitching task into multiple processes and allocating them to different cores based on the CPU core count, number of flight routes, and number of images, thereby enhancing computational efficiency in edge scenarios. The experimental results indicate that, when the number of flight routes is greater than or equal to the number of CPUs, the adaptive algorithm will employ the more efficient route parallelism. Conversely, when the number of flight routes is less than the number of CPUs, the efficiency of inverted binary tree parallelism is higher. In the same experimental environment and dataset, the adaptive image stitching algorithm demonstrates an efficiency improvement of approximately 2–10 times compared to other algorithms, with no significant degradation in image quality. This demonstrates that in edge environments, the utilization of multi-threaded adaptive route and inverted binary tree-based parallel approaches can effectively harness the computing resources of edge devices, significantly improving the stitching speed of UAV images and providing technical support for rapid real-time monitoring by UAV.

A Real-Time Localization Algorithm for Unmanned Aerial Vehicle Based on Continuous Images Processing

A Real-Time Localization Algorithm for Unmanned Aerial Vehicle Based on Continuous Images Processing

Unmanned Aerial Vehicle Path-Planning Method Based on Improved P-RRT* Algorithm

This paper proposed an improved potential rapidly exploring random tree star (P-RRT*) algorithm for unmanned aerial vehicles (UAV). The algorithm has faster expansion and convergence speeds and better path quality. Path planning is an important part of the UAV control system. Rapidly exploring random tree (RRT) is a path-planning algorithm that is widely used, including in UAV, and its altered body, P-RRT*, is an asymptotic optimal algorithm with bias sampling. The algorithm converges slowly and has a large random sampling area. To overcome the above drawbacks, we made the following improvements. First, the algorithm used the direction of the artificial potential field (APF) to determine whether to perform greedy expansion, increasing the search efficiency. Second, as the random tree obtained the initial path and updated the path cost, the algorithm rejected high-cost nodes and sampling points based on the heuristic cost and current path cost to speed up the convergence rate. Then, the random tree was pruned to remove the redundant nodes in the path. The simulation results demonstrated that the proposed algorithm could significantly decrease the path cost and inflection points, speed up initial path obtaining and convergence, and is suitable for the path planning of UAVs.

Aerodynamic Characterization of a Fan-Array Wind Generator

Experimental assessment of safe and precise flight control algorithms for unmanned aerial vehicles under gusty wind conditions requires the capability to generate a large range of velocity profiles. In this study, we employ a small fan-array wind generator that can generate flows with large spatial and temporal variabilities. We perform a thorough aerodynamic characterization, operating the fans uniformly from low to maximum levels. Particle image velocimetry and hot-wire measurements indicate a jetlike flow with a nearly uniform core, which monotonously contracts in the streamwise direction and the surrounding, growing unsteady shear layers. These complex dynamics result in a limited region with a desired flow profile and turbulence level. The experimental results shed light on the flow generated by a full-scale fan-array wind generator, and they indicate the need for further improvements via properly designed add-ons and dedicated control algorithms.

The development status and analysis of motion control algorithms applied to UAVs

Due to the high mobility and longer durability, UAVs are widely used in military and various civilian fields. The control system of the UAVs is a key part of ensuring that the UAVs fulfil various instructions and complete the tasks. In response to the above issues, this paper summarizes and analyses the current status of motion control algorithms for unmanned aerial vehicles based on existing data. Firstly, the mainstream control technology of UAVs is divided into linear control technology, nonlinear control technology and machine learning-based control technology, and they are discussed separately. Subsequently, the performance of three technologies is evaluated and compared, and the advantages, disadvantages and the applicable environment of each controller are introduced. Finally, the future development direction of UAVs controller is analysed and prospected.

A robust multiple Unmanned Aerial Vehicles 3D path planning strategy via improved particle swarm optimization

Path planning algorithms for Unmanned Aerial Vehicles (UAVs) are essential in various domains, such as search and rescue operations, agriculture, and delivery services. Nonetheless, finding optimal flight paths in complex environments with obstacles remains challenging. This paper proposes a novel path planning algorithm that uses a Nash equilibrium approach based on game theory to strike a balance between the exploitation and exploration capabilities of UAVs. The algorithm is designed in a simulated flight environment with obstacles, utilizing the self-awareness and group awareness coefficients from particle swarm optimization(PSO) as participants in the game theory model. The proposed path planning algorithm significantly improves the navigation of multiple UAVs in complex scenarios with obstacles by achieving a balance between their exploitation and exploration capabilities. Simulation experiments demonstrate that the proposed method surpasses traditional approaches, exhibiting an average improvement of 32.57%, 32.17%, and 29.33% in the algorithm convergence time, average flight distance, and average flight time, respectively. The significance of this research lies in its contribution to advancing UAV path planning algorithms. By integrating game theory and PSO, the proposed method optimizes the exploration and exploitation capabilities of UAVs, leading to improved navigation and resource utilization.

A Real-Time Path Planning Method for Urban Low-Altitude Logistics UAVs.

To solve the problem of poor real-time performance in path planning algorithms for unmanned aerial vehicles (UAVs) in low-altitude urban logistics, a path planning method combining modified Beetle Antennae Search (BAS) with the Simulated Annealing (SA) algorithm is proposed. Firstly, based on the requirements of task execution and constraints of UAV flight, a fitness function for real-time search of waypoints is designed while ensuring the safety and obstacle avoidance of the UAV. Then, to improve the search accuracy and real-time performance, determining the initial search direction in the BAS algorithm is improved, while the search step size and antennae sensing length are updated in real-time according to the distance between the UAV and the obstacle. Finally, the SA algorithm is combined with the BAS algorithm to update the waypoints, expanding the search range of each waypoint, avoiding the process of updating the waypoints from becoming trapped in the local optimal waypoints. Meanwhile, the effectiveness of the next waypoint is evaluated based on the Metropolis criterion. This paper generates a virtual urban logistics distribution environment based on the density and distribution of urban buildings, and compares the performance of algorithms in obstacle-sparse, obstacle-moderate, and obstacle-dense environments. The simulation results demonstrate that the improved method in this paper has a more significant capacity for environmental adaptation. In terms of the path length, waypoints, safety obstacle avoidance, and smoothness, the planned path outperforms the original BAS method. It satisfies the needs of real-time path planning for UAVs involved in urban low-altitude logistics.

Collision-Free Model Predictive Trajectory Tracking Control for UAVs in Obstacle Environment

In this paper, we propose a collision-free model predictive trajectory tracking control algorithm for unmanned aerial vehicles (UAVs) in environments with both static obstacles and dynamic obstacles. Collision avoidance is ensured by obtaining outer polyhedral approximations of each interval of the dynamic obstacles trajectories based on MINVO basis, and then optimizing a plane to separate the polyhedra and the trajectory of the UAV. By incorporating the resulting computationally efficient collisionfree constraints and divers physical constraints, a model predictive control (MPC) optimization problem is formulated with a tailored terminal constraint set, which can be solved by a standard nonlinear programming solver. Moreover, the control theoretic properties are established, including recursive feasibility, the guarantee of collision avoidance, as well as closed-loop stability. Finally, the efficacy of the proposed algorithm is successfully evaluated by a simulation in a multi-obstacle environment.