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27 pages, 13716 KiB

Open AccessArticle

Short Landing Control Techniques Using Optimization of Flare Time Constant for High-Speed Fixed-Wing UAV

by Ryoga Sakaki and Masazumi Ueba

Aerospace 2025, 12(4), 318; https://doi.org/10.3390/aerospace12040318 - 8 Apr 2025

Viewed by 244

Abstract

In recent years, the use of unmanned aerial vehicles (UAVs) has expanded in and across various fields, including agriculture, observation, and transportation. Generally, the landing distance of fixed-wing UAVs increases with speed. In particular, the landing distance in the flare phase is proportional [...] Read more.

In recent years, the use of unmanned aerial vehicles (UAVs) has expanded in and across various fields, including agriculture, observation, and transportation. Generally, the landing distance of fixed-wing UAVs increases with speed. In particular, the landing distance in the flare phase is proportional to the flight speed. To expand the range of applications for missions by the UAV, it is necessary to develop a short-distance landing control technique. This study focuses on reducing the landing distance during the flare phase before touchdown. The flare path is dominated by the flare time constant. The smaller the flare time constant, the greater the curvature of the flight path and the shorter the horizontal distance. Therefore, we propose a method to determine the flare time constant by applying a nonlinear optimization in which the horizontal distance during the flare phase is used as the evaluation function. The method uses a motion model that incorporates both translational and rotational motion in the longitudinal direction, which is more comprehensive than a point mass model. After solving the nonlinear optimization problem to obtain the flare time constant, we first conduct longitudinal flight simulation to confirm both the accuracy of the optimal solution and the validity of the motion model used in the nonlinear optimization problem and, then, confirm the feasibility of the landing control technique with the optimized flare time constant using a six-degrees-of-freedom simulation. Full article

(This article belongs to the Special Issue UAV System Modelling Design and Simulation)

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19 pages, 7338 KiB

Open AccessArticle

The Design and Evaluation of a Direction Sensor System Using Color Marker Patterns Onboard Small Fixed-Wing UAVs in a Wireless Relay System

by Kanya Hirai and Masazumi Ueba

Aerospace 2025, 12(3), 216; https://doi.org/10.3390/aerospace12030216 - 7 Mar 2025

Viewed by 450

Abstract

Among the several usages of unmanned aerial vehicles (UAVs), a wireless relay system is one of the most promising applications. Specifically, a small fixed-wing UAV is suitable to establish the system promptly. In the system, an antenna pointing control system directs an onboard [...] Read more.

Among the several usages of unmanned aerial vehicles (UAVs), a wireless relay system is one of the most promising applications. Specifically, a small fixed-wing UAV is suitable to establish the system promptly. In the system, an antenna pointing control system directs an onboard antenna to a ground station in order to form and maintain a communication link between the UAV and the ground station. In this paper, we propose a sensor system to detect the direction of the ground station from the UAV by using color marker patterns for the antenna pointing control system. The sensor detects the difference between the antenna pointing direction and the ground station direction. The sensor is characterized by the usage of both the color information of multiple color markers and color marker pattern matching. These enable the detection of distant, low-resolution markers, a high accuracy of marker detection, and robust marker detection against motion blur. In this paper, we describe the detailed algorithm of the sensor, and its performance is evaluated by using the prototype sensor system. Experimental performance evaluation results showed that the proposed method had a minimum detectable drawing size of 10.2 pixels, a motion blur tolerance of 0.0175, and a detection accuracy error of less than 0.12 deg. This performance indicates that the method has a minimum detectable draw size that is half that of the ArUco marker (a common AR marker), is 15.9 times more tolerant of motion blur than the ArUco marker, and has a detection accuracy error twice that of the ArUco marker. The color markers in the proposed method can be placed farther away or be smaller in size than ArUco markers, and they can be detected by the onboard camera even if the aircraft’s attitude changes significantly. The proposed method using color marker patterns has the potential to improve the operational flexibility of radio relay systems utilizing UAVs and is expected to be further developed in the future. Full article

(This article belongs to the Special Issue UAV System Modelling Design and Simulation)

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23 pages, 27451 KiB

Open AccessArticle

Adapted Speed Control of Two-Stroke Engine with Propeller for Small UAVs Based on Scavenging Measurement and Modeling

by Yifang Feng, Tao Chen, Qinwang Liu and Heng Zhao

Aerospace 2025, 12(3), 202; https://doi.org/10.3390/aerospace12030202 - 28 Feb 2025

Viewed by 656

Abstract

The speed of the engine–propeller directly determines the power output for Unmanned Aerial Vehicles (UAV) with internal combustion engines. However, variable air pressure can impact the engine’s air exchange and combustion processes, causing minor changes that affect the engine speed and result in [...] Read more.

The speed of the engine–propeller directly determines the power output for Unmanned Aerial Vehicles (UAV) with internal combustion engines. However, variable air pressure can impact the engine’s air exchange and combustion processes, causing minor changes that affect the engine speed and result in variations in propeller thrust. A single-loop control strategy was proposed incorporating a feed-forward air-intake model with throttle feedback for small UAVs equipped with a two-stroke scavenging internal combustion engine and propeller. The feed-forward model was built with a simplified model of the airpath based on the scavenging measurement, which combined the tracer gas method and CFD simulation by a two-zone combustion chamber model. The feed-forward control strategy was built by a simplified crankcase–scavenging–cylinder model with CFD results under different air pressures, demonstrating a 1% error compared with CFD simulation. An iterative method of feed-forwarding was suggested for computing efficiency. A feedback controller was constructed using fuzzy PID for minimal instrumentation in engine control for small aircraft. Finally, the single-loop control strategy was validated through simulation and experimentation. The results indicate an 89% reduction in average speed error under varying air pressure and an 83.7% decrease in average speed overshoot in continuous step speed target experiments. Full article

(This article belongs to the Special Issue UAV System Modelling Design and Simulation)

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25 pages, 9276 KiB

Open AccessArticle

Experimental Evaluation of Multi- and Single-Drone Systems with 1D LiDAR Sensors for Stockpile Volume Estimation

by Ahmad Alsayed, Fatemeh Bana, Farshad Arvin, Mark K. Quinn and Mostafa R. A. Nabawy

Aerospace 2025, 12(3), 189; https://doi.org/10.3390/aerospace12030189 - 26 Feb 2025

Viewed by 723

Abstract

This study examines the application of low-cost 1D LiDAR sensors in drone-based stockpile volume estimation, with a focus on indoor environments. Three approaches were experimentally investigated: (i) a multi-drone system equipped with static, downward-facing 1D LiDAR sensors combined with an adaptive formation control [...] Read more.

This study examines the application of low-cost 1D LiDAR sensors in drone-based stockpile volume estimation, with a focus on indoor environments. Three approaches were experimentally investigated: (i) a multi-drone system equipped with static, downward-facing 1D LiDAR sensors combined with an adaptive formation control algorithm; (ii) a single drone with a static, downward-facing 1D LiDAR following a zigzag trajectory; and (iii) a single drone with an actuated 1D LiDAR in an oscillatory fashion to enhance scanning coverage while following a shorter trajectory. The adaptive formation control algorithm, newly developed in this study, synchronises the drones’ waypoint arrivals and facilitates smooth transitions between dynamic formation shapes. Real-world experiments conducted in a motion-tracking indoor facility confirmed the effectiveness of all three approaches in accurately completing scanning tasks, as per intended waypoints allocation. A trapezoidal prism stockpile was scanned, and the volume estimation accuracy of each approach was compared. The multi-drone system achieved an average volumetric error of 1.3%, similar to the single drone with a static sensor, but with less than half the flight time. Meanwhile, the actuated LiDAR system required shorter paths but experienced a higher volumetric error of 4.4%, primarily due to surface reconstruction outliers and common LiDAR bias when scanning at non-vertical angles. Full article

(This article belongs to the Special Issue UAV System Modelling Design and Simulation)

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15 pages, 7626 KiB

Open AccessArticle

Determination of the Tail Unit Parameters of Ultralight Manned and Unmanned Helicopters at the Preliminary Design Stage

by Vitaly Dudnik

Aerospace 2025, 12(1), 33; https://doi.org/10.3390/aerospace12010033 - 8 Jan 2025

Viewed by 830

Abstract

The 1–2 seat helicopters have developed considerably in recent years. They have a maximum take-off weight of 250 to 750 kg. Most of these helicopters have been converted into unmanned versions. Typically, such UAV models retain the rotor system, power plant, transmission, and [...] Read more.

The 1–2 seat helicopters have developed considerably in recent years. They have a maximum take-off weight of 250 to 750 kg. Most of these helicopters have been converted into unmanned versions. Typically, such UAV models retain the rotor system, power plant, transmission, and empennage of the manned versions. For this reason, statistics and design methods for small manned helicopters are also applied to unmanned versions. The existing methods for selecting helicopter parameters in the preliminary design phase are based on statistical data for heavier-class helicopters. However, the lightest weight class helicopters differ significantly from their heavier counterparts. The analysis shows that the results of parameter selection at the preliminary design stage have an error rate of between 11 and 30%. The main reason for this difference is a scale factor. In this paper, a method for determining helicopter tail unit parameters at the preliminary design stage is presented. The proposed relationships for the horizontal stabilizer, fin, tail boom, and tail rotor parameters are based on an analysis of statistical data from 36 rotorcraft and the author’s design experience. In particular, the article presents the relationships between the geometric parameters of the empennage and tail rotor from other helicopter data. The relationships presented also allow the mass of the tail unit to be determined. Full article

(This article belongs to the Special Issue UAV System Modelling Design and Simulation)

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16 pages, 481 KiB

Open AccessArticle

Topology Perception and Relative Positioning of UAV Swarm Formation Based on Low-Rank Optimization

by Chengliang Di and Xiaozhou Guo

Aerospace 2024, 11(6), 466; https://doi.org/10.3390/aerospace11060466 - 11 Jun 2024

Cited by 2 | Viewed by 1840

Abstract

In a satellite-denied environment, a swarm of drones is capable of achieving relative positioning and navigation by leveraging the high-precision ranging capabilities of the inter-drone data link. However, because of factors such as high drone mobility, complex and time-varying channel environments, electromagnetic interference, [...] Read more.

In a satellite-denied environment, a swarm of drones is capable of achieving relative positioning and navigation by leveraging the high-precision ranging capabilities of the inter-drone data link. However, because of factors such as high drone mobility, complex and time-varying channel environments, electromagnetic interference, and poor communication link quality, distance errors and even missing distance values between some nodes are inevitable. To address these issues, this paper proposes a low-rank optimization algorithm based on the eigenvalue scaling of the distance matrix. By gradually limiting the eigenvalues of the observed distance matrix, the algorithm reduces the rank of the matrix, bringing the observed distance matrix closer to the true value without errors or missing data. This process filters out distance errors, estimates and completes missing distance elements, and ensures high-precision calculations for subsequent topology perception and relative positioning. Simulation experiments demonstrate that the algorithm exhibits significant error filtering and missing element completion capabilities. Using the F-norm metric to measure the relative deviation from the true value, the algorithm can optimize the relative deviation of the observed distance matrix from 11.18% to 0.25%. Simultaneously, it reduces the relative positioning error from 518.05 m to 35.24 m, achieving robust topology perception and relative positioning for the drone swarm formation. Full article

(This article belongs to the Special Issue UAV System Modelling Design and Simulation)

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36 pages, 22100 KiB

Open AccessArticle

Modeling Wind and Obstacle Disturbances for Effective Performance Observations and Analysis of Resilience in UAV Swarms

by Abhishek Phadke, F. Antonio Medrano, Tianxing Chu, Chandra N. Sekharan and Michael J. Starek

Aerospace 2024, 11(3), 237; https://doi.org/10.3390/aerospace11030237 - 18 Mar 2024

Cited by 20 | Viewed by 3537

Abstract

UAV swarms have multiple real-world applications but operate in a dynamic environment where disruptions can impede performance or stop mission progress. Ideally, a UAV swarm should be resilient to disruptions to maintain the desired performance and produce consistent outputs. Resilience is the system’s [...] Read more.

UAV swarms have multiple real-world applications but operate in a dynamic environment where disruptions can impede performance or stop mission progress. Ideally, a UAV swarm should be resilient to disruptions to maintain the desired performance and produce consistent outputs. Resilience is the system’s capability to withstand disruptions and maintain acceptable performance levels. Scientists propose novel methods for resilience integration in UAV swarms and test them in simulation scenarios to gauge the performance and observe the system response. However, current studies lack a comprehensive inclusion of modeled disruptions to monitor performance accurately. Existing approaches in compartmentalized research prevent a thorough coverage of disruptions to test resilient responses. Actual resilient systems require robustness in multiple components. The challenge begins with recognizing, classifying, and implementing accurate disruption models in simulation scenarios. This calls for a dedicated study to outline, categorize, and model interferences that can be included in current simulation software, which is provided herein. Wind and in-path obstacles are the two primary disruptions, particularly in the case of aerial vehicles. This study starts a multi-step process to implement these disruptions in simulations accurately. Wind and obstacles are modeled using multiple methods and implemented in simulation scenarios. Their presence in simulations is demonstrated, and suggested scenarios and targeted observations are recommended. The study concludes that introducing previously absent and accurately modeled disruptions, such as wind and obstacles in simulation scenarios, can significantly change how resilience in swarm deployments is recorded and presented. A dedicated section for future work includes suggestions for implementing other disruptions, such as component failure and network intrusion. Full article

(This article belongs to the Special Issue UAV System Modelling Design and Simulation)

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Review

Jump to: Research

33 pages, 6205 KiB

Open AccessReview

Hybrid Machine Learning and Reinforcement Learning Framework for Adaptive UAV Obstacle Avoidance

by Wojciech Skarka and Rukhseena Ashfaq

Aerospace 2024, 11(11), 870; https://doi.org/10.3390/aerospace11110870 - 24 Oct 2024

Cited by 1 | Viewed by 5605

Abstract

This review explores the integration of machine learning (ML) and reinforcement learning (RL) techniques in enhancing the navigation and obstacle avoidance capabilities of Unmanned Aerial Vehicles (UAVs). Various RL algorithms are assessed for their effectiveness in teaching UAVs autonomous navigation, with a focus [...] Read more.

This review explores the integration of machine learning (ML) and reinforcement learning (RL) techniques in enhancing the navigation and obstacle avoidance capabilities of Unmanned Aerial Vehicles (UAVs). Various RL algorithms are assessed for their effectiveness in teaching UAVs autonomous navigation, with a focus on state representation from UAV sensors and real-time environmental interaction. The review identifies the strengths and limitations of current methodologies and highlights gaps in the literature, proposing future research directions to advance UAV technology. Interdisciplinary approaches combining robotics, AI, and aeronautics are suggested to improve UAV performance in complex environments. Full article

(This article belongs to the Special Issue UAV System Modelling Design and Simulation)

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