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1
N., M. Salma, and Osman Khairuddin. "Modelling and PID control system integration for quadcopter DJIF450 attitude stabilization." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 19, no. 3 (2020): 1235–44. https://doi.org/10.11591/ijeecs.v19.i3.pp1235-1244.
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In this paper we focus on the overall overview of the mathematical modelling of the DJI F450 UAV quadcopter, the hardware and software system integration based on PID control system for the attitude feedback. The parameter specification of the DJI F450 is fed into the mathematical model and implement a basic PID for the system. Future research using the DJI F450 model can benefit from this observation by implementing the modelling and tune in their own variable that varies, such as the overall of their weight. The data of PID control system simulation using the quadcopter frame model type DJI F450 parameter. The mathematical model for the quadcopter modelled DJI F450 is developed using Newton-Euler method. Altitude data for the control system is obtain from the analysis data of the Simulink simulation. The simulation is done using the Simulink toolbox inside the MATLAB software. From this paper, we can more understand the step involves in making a full control system of a quadcopter. The mathematical model for other type of quadcopter model can be implemented using the steps with their own parameter and achieve fast development.
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Salma, N. M., and Khairuddin Osman. "Modelling and PID control system integration for quadcopter DJI F450 attitude stabilization." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 3 (2020): 1235. http://dx.doi.org/10.11591/ijeecs.v19.i3.pp1235-1244.
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<p><span style="font-family: Times New Roman;">In this paper we focus on the overall overview of the mathematical modelling of the DJI F450 UAV quadcopter, the hardware and software system integration based on PID control system for the attitude feedback. The parameter specification of the DJI F450 is fed into the mathematical model and implement a basic PID for the system. Future research using the DJI F450 model can benefit from this observation by implementing the modelling and tune in their own variable that varies, such as the overall of their weight. The data of PID control system simulation using the quadcopter frame model type DJI F450 parameter. The mathematical model for the quadcopter modelled DJI F450 is developed using Newton-Euler method. Altitude data for the control system is obtain from the analysis data of the Simulink simulation. The simulation is done using the Simulink toolbox inside the MATLAB software. From this paper, we can more understand the step involves in making a full control system of a quadcopter. The mathematical model for other type of quadcopter model can be implemented using the steps with their own parameter and achieve fast development.</span></p>
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Malothu, Kirankumar, Santhoshchandra Rayudu, and Uday Kumar R. "Developing a Quadcopter Frame and its Structural Analysis." International Journal of Innovative Science and Research Technology 8, no. 1 (2023): 496–501. https://doi.org/10.5281/zenodo.7568657.
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A quadcopter is one of the most versatile unmanned aerial vehicles (UAV) used for a variety of tasks. Quadcopters are symmetrical and use the simplest principle of operation for controlling roll, pitch, yaw, and motion. A DJI F450 quadcopter frame design with optimized geometry is used in this project.All the frame's components were designed using Fusion 360.Based on the results of stress analysis with Fusion 360, the proposed design is validated for its feasibility, and a suitable material for fabrication is selected through comparison with five other materials.In this paper, we investigate the static stress in the quadcopter frame and the modal frequencies in the quadcopter arm.
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Bright, Jerrin, R. Suryaprakash, S. Akash, and A. Giridharan. "Optimization of quadcopter frame using generative design and comparison with DJI F450 drone frame." IOP Conference Series: Materials Science and Engineering 1012 (January 8, 2021): 012019. http://dx.doi.org/10.1088/1757-899x/1012/1/012019.
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Bright, Jerrin, R. Suryaprakash, S. Akash, and A. Giridharan. "Optimization of quadcopter frame using generative design and comparison with DJI F450 drone frame." IOP Conference Series: Materials Science and Engineering 1012 (January 8, 2021): 012019. http://dx.doi.org/10.1088/1757-899x/1012/1/012019.
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Xuan-Mung, Nguyen, and Sung-Kyung Hong. "Improved Altitude Control Algorithm for Quadcopter Unmanned Aerial Vehicles." Applied Sciences 9, no. 10 (2019): 2122. http://dx.doi.org/10.3390/app9102122.
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Quadcopter unmanned aerial vehicles continue to play important roles in several applications and the improvement of their control performance has been explored in a great number of studies. In this paper, we present an altitude control algorithm for quadcopters that consists of a combination of nonlinear and linear controllers. The smooth transition between the nonlinear and linear modes are guaranteed through controller gains that are obtained based on mathematical analysis. The proposed controller takes advantage and addresses some known shortcomings of the conventional proportional–integral–derivative control method. The algorithm is simple to implement, and we prove its stability through the Lyapunov theory. By prescribing certain flight conditions, we use numerical simulations to compare the control performance of our control method to that of a conventional proportional–derivative–integral approach. Furthermore, we use a DJI-F450 drone equipped with a laser ranging sensor as the experimental quadcopter platform to evaluate the performance of our new controller in real flight conditions. Numerical simulation and experimental results demonstrate the effectiveness of the proposed algorithm.
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Suparta, W., A. Basuki, and M. Arsyad. "The development of quadcopter using Arducopter APM 2.8 with autopilot for tracking point of drop-off goods." IOP Conference Series: Earth and Environmental Science 1151, no. 1 (2023): 012032. http://dx.doi.org/10.1088/1755-1315/1151/1/012032.
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Abstract This research reported the development of a drone withinside the shape of a quadrotor to track positions over a certain geographic area on autopilot. The system includes an Ardupilot Mega (APM) 2.8 flight controller, Ublox NEO M8N GPS module with compass, Racerstar 920kV 2-4S Brushless Motor, Flysky Receiver FS-iA6B with FS-i6 Remote Control Transmitter, DJI F450 quadcopter frame kits with touchdown tools skid, and a LiPo Battery 3300 mAh 35C. The ground control system is set up and run through an open-source software so-known as Mission Planner. The tracking system is evolved as a payload together with BME280 sensors controlled through Arduino Uno R3 SMD. Google Maps from Mission Planner are set via waypoints. The readings from the BME280 barometer sensor are used to examine the right coordinates on the waypoint. This correction may be very crucial while a drone is implemented to a delivery system. The results show that the average error at every waypoint is 5%, which shows that one waypoint can be used as a drop-off point factor for goods to customers.
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Suparta, Wayan, and Trie Handayani. "Development of Quadcopter for Atmospheric Data Collection." JURNAL INFOTEL 14, no. 1 (2022): 57–64. http://dx.doi.org/10.20895/infotel.v14i1.727.
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This research aims to develop a quadrotor system as unmanned aircraft vehicles (UAVs, or drones) for monitoring atmospheric conditions in a targeted area. The system consists of an APM 2.8 arducopter flight controller, Ublox NEO M8N GPS module with compass, Racerstar 920kV 2-4S Brushless Motor, Flysky Receiver FS-iA6B with FS-i6 Remote Control Transmitter, DJI F450 quadcopter frame kits with tall landing gear skid, and a LiPo Battery 3300 mAh 35C. The system is set up and run through a Mission Planner. As for monitoring atmospheric conditions, the system consists of an Arduino Uno ATmega328P, BME280 sensors, and several modules (DS3231 Real-Time Clock (RTC), micro SD card, and 16×2 LCD). Our vehicle with a total weight of 1 kg can fly into space and maneuver to an altitude of more than 200 meters in an average of 10 minutes. Atmospheric conditions such as air temperature, relative humidity, air pressure, altitude, and precipitable water vapor can be measured and logged properly from drones. By this development, the system can be applied in the future to detect or measure weather extremes, air pollution, or monitoring aerial topography automatically when equipped with gas sensors and cameras, respectively.
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Indreswari, Suroso, and Irmawan Erwhin. "Analysis of UAV multicopter of air photography in New Yogyakarta International Airports." TELKOMNIKA Telecommunication, Computing, Electronics and Control 17, no. 1 (2019): 521–28. https://doi.org/10.12928/TELKOMNIKA.v17i1.9255.
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The higher the quality of the drone, the longer the drone will fly and the better the quality of the drone's photography. Survey of research location in Glagah Indah Beach, preparation of drone at ground, we plan the height of flying drones, then testing drone at ground, we measure camera calibration, and then result capture in the air and images in the air. Vehicle specifications are as follows: Frame: F450; Flight Controller: DJI Naza M-Lite; Propeller: 1045 Prop; motorbike: brushless sunnsky 980 kVa; ESC: Skywalker 40 Ampere 3s; Battery: Ace 3s Gens 5000 mAH; Remote: Turnigy 9XR with Frsky Tanseiver; and camera: Xiaomi Yi 4k International edition.This drone type multicopter can penetrate the high of 100 meters to 200 meters and can air for 30 minutes, can take an area of up to 1 km while payload drones multicopter is 2.8 kg.This multicopter drone has a 12 megapixel sensor; maximum flight time of 15 minutes; speed of 20 m/s, maximum take-up speed of 6 m/s, maximum landing speed of 4 m/s, temperature range when operating drone 0 to 40 C and maximum image size of 4000x3000.
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Ahmed, Nigar, and Nashmi Alrasheedi. "Commanded Filter-Based Robust Model Reference Adaptive Control for Quadrotor UAV with State Estimation Subject to Disturbances." Drones 9, no. 3 (2025): 181. https://doi.org/10.3390/drones9030181.
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Unmanned aerial vehicles must achieve precise flight maneuvers despite disturbances, parametric uncertainties, modeling inaccuracies, and limitations in onboard sensor information. This paper presents a robust adaptive control for trajectory tracking under nonlinear disturbances. Firstly, parametric and modeling uncertainties are addressed using model reference adaptive control principles to ensure that the dynamics of the aerial vehicle closely follow a reference model. To address the effects of disturbances, a modified nonlinear disturbance observer is designed based on estimated state variables. This observer effectively attenuates constant, nonlinear disturbances with variable frequency and magnitude, and noises. In the next step, a two-stage sliding mode control strategy is introduced, incorporating adaptive laws and a commanded-filter to compute numerical derivatives of the state variables required for control design. An error compensator is integrated into the framework to reduce numerical and computational delays. To address sensor inaccuracies and potential failures, a high-gain observer-based state estimation technique is employed, utilizing the separation principle to incorporate estimated state variables into the control design. Finally, Lyapunov-based stability analysis demonstrates that the system is uniformly ultimately bounded. Numerical simulations on a DJI F450 quadrotor validate the approach’s effectiveness in achieving robust trajectory tracking under disturbances.
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Semenyuk, Vladislav, Ildar Kurmashev, Dmitriy Ritter, Pavel Petrov, Sayat Moldakhmetov, and Alibek Adilbekov. "Developing the GoogleNet neural network for the detection and recognition of unmanned aerial vehicles in the Data Fusion system." Eastern-European Journal of Enterprise Technologies 5, no. 9(119) (2022): 25–33. http://dx.doi.org/10.15587/1729-4061.2022.265713.
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This work reports a study into the possibility of using the GoogleNet neural network in the optoelectronic channel of the Data Fusion system. The search for the most accurate algorithms for detecting and recognizing unmanned aerial vehicles (UAVs) in Data Fusion systems has been carried out. The data processing scheme was selected (merging SVF state vectors and merging MF measurements), as well as the sensors and recognition models on each channel of the system. The Data Fusion model based on the Kalman Filter was chosen, integrating radar and optoelectronic channels. Mini-radars LPI-FMCW were used as a radar channel. Evaluation of the effectiveness of the selected Data Fusion channel model in UAV detection is based on the recognition accuracy. The main study is aimed at determining the possibility of using the GoogleNet neural network in the optoelectronic channel for UAV recognition under conditions of different range classes. The neural network for the recognition of drones was developed using transfer training technology. For training, validation, and testing of the GoogleNet neural network, a database has been built, and a special application has been developed in the MATLAB environment. The capabilities of the developed neural network were studied for 5 variants of the distance to the object. The detection objects were the Inspire 2, DJI Phantom 4 Pro, DJI F450, DU 1911 UAVs, not included in the training database. The UAV recognition accuracy by the neural network was 98.13 % at a distance of up to 5 m, 94.65 % at a distance of up to 20 m, 92.47 % at a distance of up to 50 m, 90.28 % at a distance of up to 100 m, and 88.76 % at a distance of up to 200 m. The average speed of UAV recognition by this method was 0.81 s.
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Semenyuk, Vladislav, Ildar Kurmashev, Alberto Lupidi, and Alessandro Cantelli-Forti. "Developing the GoogleNet neural network for the detection and recognition of unmanned aerial vehicles in the data Fusion System." Eastern-European Journal of Enterprise Technologies 2, no. 9 (122) (2023): 16–25. http://dx.doi.org/10.15587/1729-4061.2023.276175.
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This work reports a study into the possibility of using the GoogleNet neural network in the optoelectronic channel of the Data Fusion system. The search for the most accurate algorithms for detecting and recognizing unmanned aerial vehicles (UAVs) in Data Fusion systems has been carried out. The data processing scheme was selected (merging SVF state vectors and merging MF measurements), as well as the sensors and recognition models on each channel of the system. The Data Fusion model based on the Kalman Filter was chosen, integrating radar and optoelectronic channels. Mini-radars LPI-FMCW were used as a radar channel. Evaluation of the effectiveness of the selected Data Fusion channel model in UAV detection is based on the recognition accuracy. The main study is aimed at determining the possibility of using the GoogleNet neural network in the optoelectronic channel for UAV recognition under conditions of different range classes. The neural network for the recognition of drones was developed using transfer training technology. For training, validation, and testing of the GoogleNet neural network, a database has been built, and a special application has been developed in the MATLAB environment. The capabilities of the developed neural network were studied for 5 variants of the distance to the object. The detection objects were the Inspire 2, DJI Phantom 4 Pro, DJI F450, DU 1911 UAVs, not included in the training database. The UAV recognition accuracy by the neural network was 98.13 % at a distance of up to 5 m, 94.65 % at a distance of up to 20 m, 92.47 % at a distance of up to 20 m, 90.28 % at a distance of up to 100 m, and 88.76 % at a distance of up to 200 m. The average speed of UAV recognition by this method was 0.81 s.
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Vladislav, Semenyuk, Kurmashev Ildar, Lupidi Alberto, and Cantelli-Forti Alessandro. "Developing the GoogleNet neural network for the detection and recognition of unmanned aerial vehicles in the data Fusion System." Eastern-European Journal of Enterprise Technologies 2, no. 9(122) (2023): 16–25. https://doi.org/10.15587/1729-4061.2023.276175.
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This work reports a study into the possibility of using the GoogleNet neural network in the optoelectronic channel of the Data Fusion system. The search for the most accurate algorithms for detecting and recognizing unmanned aerial vehicles (UAVs) in Data Fusion systems has been carried out. The data processing scheme was selected (merging SVF state vectors and merging MF measurements), as well as the sensors and recognition models on each channel of the system. The Data Fusion model based on the Kalman Filter was chosen, integrating radar and optoelectronic channels. Mini-radars LPI-FMCW were used as a radar channel. Evaluation of the effectiveness of the selected Data Fusion channel model in UAV detection is based on the recognition accuracy. The main study is aimed at determining the possibility of using the GoogleNet neural network in the optoelectronic channel for UAV recognition under conditions of different range classes. The neural network for the recognition of drones was developed using transfer training technology. For training, validation, and testing of the GoogleNet neural network, a database has been built, and a special application has been developed in the MATLAB environment. The capabilities of the developed neural network were studied for 5 variants of the distance to the object. The detection objects were the Inspire 2, DJI Phantom 4 Pro, DJI F450, DU 1911 UAVs, not included in the training database. The UAV recognition accuracy by the neural network was 98.13 % at a distance of up to 5 m, 94.65 % at a distance of up to 20 m, 92.47 % at a distance of up to 20 m, 90.28 % at a distance of up to 100 m, and 88.76 % at a distance of up to 200 m. The average speed of UAV recognition by this method was 0.81 s.
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Suroso, Indreswari. "Analysis Of Mapping Multicopter Drones In The Entrance Area Of Prospective New Airports In Congot, Temon, Kulonprogo, Yogyakarta." Journal of Applied Geospatial Information 2, no. 2 (2019): 130–34. http://dx.doi.org/10.30871/jagi.v2i2.952.
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This research use multicopter drone. This mapping was carried out at the entrance area of the prospective new airport precisely at the Congot beach area, Temon district, Kulonprogo district with a multicopter drone. This drone is capable of recording an altitude of 100 meters above ground level and can photograph an area of 1.5 km. This study used a drone type multicopter The vehicle specifications are as follows: Frame: F450; Flight Controller: DJI Naza M-Lite; Propeller: 1045 Prop; motorbike: brushless sunnsky 980 kVa; ESC: Skywalker 40 Ampere 3s; Battery: Ace 3s Gens 5000mAH; Remote: Turnigy 9XR with Frsky Tanseiver; and camera: Xiaomi Yi 4k International edition. The height of a multicopter drone reaches 30 meters, can take an area of up to 1 km and a flight time of 15 minutes. The advantage of this multicopter is that it uses a DJ I Phantom camera classified as stable for the light weight drone class. So for terrain with high wind speed, this multicopter drone is still able to maintain its position in the air. The Kulonprogo Regional Government and the Congot Radar Unit really appreciate this mapping because it is very helpful in mapping the entrance of new prospective airports in Kulonprogo.
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Olenko, F. F., and S. O. Malakhov. "Modeling and development of a fundamentally new way of landing a quadcopter on inclined surfaces." Journal of Physics: Conference Series 2061, no. 1 (2021): 012109. http://dx.doi.org/10.1088/1742-6596/2061/1/012109.
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Abstract Methods and software and hardware for modeling and developing a fundamentally new way of landing a quadrocopter on inclined surfaces are discussed in the paper. The current state of the project under elaboration is conditioned and described. Various approaches to the solution are feasible due to the complexity of the considered issue. Solutions can differ both in the distribution of control functions between the ground control station and the quadcopter itself, and in the choice of principles that can be used as the basis for the control system and determine its design and dynamic characteristics. Simulation and testing processes demonstrate that reverse thrust alone can increase the landing zone of an average mass quadcopter, almost doubling the maximum tilt angle at which a landing maneuver is made, thus, allowing for a high vertical speed landing. It is clearly shown that low-power adhesion mechanisms such as electrical adhesion, switchable magnets, grippers or dry glue are activated after landing, allowing it to stay on the surface after the back thrust has ceased. This can be useful in situations where sudden interference is likely to occur. Such a result is achieved using a classic quadrocopter as DJI F450 without adding any equipment.
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Guardeño, Rafael, Manuel J. López, and Víctor M. Sánchez. "MIMO PID Controller Tuning Method for Quadrotor Based on LQR/LQG Theory." Robotics 8, no. 2 (2019): 36. http://dx.doi.org/10.3390/robotics8020036.
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In this work, a new pre-tuning multivariable PID (Proportional Integral Derivative) controllers method for quadrotors is put forward. A procedure based on LQR/LQG (Linear Quadratic Regulator/Gaussian) theory is proposed for attitude and altitude control, which suposes a considerable simplification of the design problem due to only one pretuning parameter being used. With the aim to analyze the performance and robustness of the proposed method, a non-linear mathematical model of the DJI-F450 quadrotor is employed, where rotors dynamics, together with sensors drift/bias properties and noise characteristics of low-cost commercial sensors typically used in this type of applications are considered. In order to estimate the state vector and compensate bias/drift effects in the measures, a combination of filtering and data fusion algorithms (Kalman filter and Madgwick algorithm for attitude estimation) are proposed and implemented. Performance and robustness analysis of the control system is carried out by employing numerical simulations, which take into account the presence of uncertainty in the plant model and external disturbances. The obtained results show the proposed controller design method for multivariable PID controller is robust with respect to: (a) parametric uncertainty in the plant model, (b) disturbances acting at the plant input, (c) sensors measurement and estimation errors.
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Balayan, Anurag, Rajnish Mallick, Stuti Dwivedi, Sahaj Saxena, Bisheshwar Haorongbam, and Anshul Sharma. "Optimal Design of Quadcopter Chassis Using Generative Design and Lightweight Materials to Advance Precision Agriculture." Machines 12, no. 3 (2024): 187. http://dx.doi.org/10.3390/machines12030187.
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This research addresses the imperative challenge of a lightweight design for an Unmanned Aerial Vehicle (UAV) chassis to enhance the thrust-to-weight and power-to-weight ratios, crucial for optimal flight performance, focused on developing an intriguing lightweight yet robust quadcopter chassis. Advanced generative design techniques, integrated with topology optimization, using Autodesk Fusion 360 software (v. 16.5. 0.2083), 3D-printing methods and lightweight materials like Polylactic Acid (P.L.A.), Acrylonitrile Butadiene Styrene (A.B.S.), and Nylon 6/6 play a significant role in achieving the desired balance between structural integrity and weight reduction. The study showcases successful outcomes, presenting quadcopter chassis designs that significantly improve structural efficiency and overall performance metrics. The findings contribute to aerial robotics and hold promise for precision agriculture applications with relevant performed simulations, emphasizing the importance of tailored design methodologies for other engineering domains. In conclusion, this research provides a foundational step toward advancing drone technology, with weight reductions of almost 50%, P/W and T/W ratios increment of 6.08% and 6.75%, respectively, at least an 11.8% increment in Factor of Safety, at least a 70% reduction in stress values and reduced manufacturing time from its comparative DJI F450 drone, demonstrating the critical role of innovative design approaches in optimizing operational efficiency for targeted applications.
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Titu, Md Fahim Shahoriar, Mahir Afser Pavel, Goh Kah Ong Michael, Hisham Babar, Umama Aman, and Riasat Khan. "Real-Time Fire Detection: Integrating Lightweight Deep Learning Models on Drones with Edge Computing." Drones 8, no. 9 (2024): 483. http://dx.doi.org/10.3390/drones8090483.
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Fire accidents are life-threatening catastrophes leading to losses of life, financial damage, climate change, and ecological destruction. Promptly and efficiently detecting and extinguishing fires is essential to reduce the loss of lives and damage. This study uses drone, edge computing, and artificial intelligence (AI) techniques, presenting novel methods for real-time fire detection. This proposed work utilizes a comprehensive dataset of 7187 fire images and advanced deep learning models, e.g., Detection Transformer (DETR), Detectron2, You Only Look Once YOLOv8, and Autodistill-based knowledge distillation techniques to improve the model performance. The knowledge distillation approach has been implemented with the YOLOv8m (medium) as the teacher (base) model. The distilled (student) frameworks are developed employing the YOLOv8n (Nano) and DETR techniques. The YOLOv8n attains the best performance with 95.21% detection accuracy and 0.985 F1 score. A powerful hardware setup, including a Raspberry Pi 5 microcontroller, Pi camera module 3, and a DJI F450 custom-built drone, has been constructed. The distilled YOLOv8n model has been deployed in the proposed hardware setup for real-time fire identification. The YOLOv8n model achieves 89.23% accuracy and an approximate frame rate of 8 for the conducted live experiments. Integrating deep learning techniques with drone and edge devices demonstrates the proposed system’s effectiveness and potential for practical applications in fire hazard mitigation.
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Pulungan, Ali Basrah, Zaki Yuanda Putra, Adam Rasyid Sidiqi, Hamdani Hamdani, and Kathleen E. Parigalan. "Drone Kit-Python for Autonomous Quadcopter Navigation." JOIV : International Journal on Informatics Visualization 8, no. 3 (2024): 1287. http://dx.doi.org/10.62527/joiv.8.3.2301.
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Using Python scripts over the MAVLink protocol, developers can use the open-source DroneKit Python software framework to enable autonomous drone operations. This framework provides excellent flexibility and power to facilitate automated drone control. The built quadcopter has an X configuration and uses a DJI F450 frame with some modifications. Interestingly, the drone has legs made of aluminum on both sides to help with smooth takeoffs and landings. The frame is 45 cm diagonal length and 30 cm vertical height. The drone was given an additional weight in a 15 x 18 x 12.5 cm box. The propeller used in this investigation is a 9x6 carbon-based model. The x2216 1400kV brushless motor that is being used is from Sunnysky, and it comes with an Electronic Speed Controller (ESC) with a 30A rating. A 4-cell 14.8V Lithium-Polymer (Li-Po) battery with a 7200mAh capacity powers the drone. Apart from that, the drone weighs 1573g in total. The results are obtained by self-measurement and flight measurement data (FMU). Six attempts were made, and the results showed that the second flight had the longest flight time and the highest altitude. In particular, the Flight Measurement Unit (FMU) reported that the flight lasted 81 seconds and reached an altitude of 0.93 meters. In contrast, the self-measurement data reported that the flight lasted 85 seconds and reached an altitude of 1.5 meters.
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Suroso, Indreswari. "Analisis Foto Udara dengan Multicopter di Daerah Penghasil Pasir Besi Karangwuni, Temon, Kulon Progo." Rekam 16, no. 1 (2020): 29–35. http://dx.doi.org/10.24821/rekam.v16i1.3474.
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Aerial Photo Analysis Using Multicopters in the Iron-Sand Producing Area in Karangwuni, Temon, Kulon Progo. This research was conducted in areas that have the potential for iron-sand, which is in Karangwuni village, Temon Subdistrict, Kulon Progo regency. The aim of this research is to examine the surface of the largest iron-sand producing areas in Kulon Progo. The specifications of the drones used in this research are as follows: frame :F450Flight Control: DJI Naza M-Lite; Propeller: 1045 Prop; Motorcycle: brushless sunnsky 980 kVa; ESC: Skywalker 40 Ampere 3s; Battery: Ace 3s Gens 5000mAH; Remote: Turnigy 9XR together with Frsky Tanseiver; and Camera: Xiaomi Yi 4k international edition. The drone made the mapping by recording the surface of the area. Once the drone was assembled, it was tested to fly. When the drone has flown perfectly, a camera was added on the lower side. So, the image of the surface were mapped using the camera which was attached to the drone. Before mapping the area using the drone, drone was tested again. The initial step of assembling was to choose the component. The drone could fly up to 70 meters until 100 meters with a duration up to 10 minutes using the payload drone multicopter weighed 1.5 kilograms. The result of this aerial photo analysis on mapping the largest iron-sand producing area in Kulon Progo regency showed that the area mapped are very sandy and very arid, therefore it cannot be used for an agricultural land. The government of Kulon Progo regency sets a regulation that this area could be used as an iron-sand mining because this land is no longer suitable for an agricultural land as in previous times. ABSTRAKPenelitian ini dilakukan di daerah yang memiliki potensi pasir besi, yaitu di daerah Karangwuni, Kecamatan Temon, Kabupaten Kulon Progo. Tujuan penelitian ini adalah untuk meneliti permukaan wilayah daerah penghasil pasir besi terbesar di Kulon Progo. Spesifikasi drone yang digunakan penelitian ini adalah dengan spesifikasi frame: F450; pengendali penerbangan: DJI Naza M-Lite; propeller: 1045 Prop; sepeda motor: brushless sunnsky 980 kVa; ESC: Skywalker 40 Ampere 3s; baterai: Ace 3s Gens 5000mAH; remote: Turnigy 9XR dengan Frsky Tanseiver; dan kamera: Xiaomi Yi 4k edisi Internasional. Cara drone melakukan pemetaan adalah dengan merekam gambar permukaan wilayah. Drone selesai dirakit, lalu diuji terbang. Jika drone telah terbang dengan sempurna, dilanjutkan dengan penambahan kamera di sisi bawah. Gambar permukaan area menggunakan kamera yang dipasang pada drone. Sebelum memetakan dengan drone, drone terlebih dahulu diuji lagi. Tahap awal perakitan adalah pemilihan komponen. Drone ini memiliki ketinggian dari 70 hingga 100 m dengan durasi hingga 10 menit menggunakan payload drone multicopter 1,5 kg. Drone ini menggunakan kamera DJI Naza M-Lite sehingga drone dapat memotret area seluas 1,5 km. Hasil penelitian foto udara pada pemetaan di daerah penghasil pasir besi terbesar di Kulon Progo ini adalah ternyata daerah tersebut berpasir dan sangat gersang sehingga tidak dapat dijadikan lahan pertanian. Pemerintah Kulon Progo memberikan izin untuk penambangan pasir besi dikarenakan lahan ini sudah tidak cocok untuk lahan pertanian seperti dahulu lagi.
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Bürling, Kathrin, Mauricio Hunsche, Georg Noga, Lutz Pfeifer, and Lutz Damerow. "UV-induced fluorescence spectra and lifetime determination for detection of leaf rust (Puccinia triticina) in susceptible and resistant wheat (Triticum aestivum) cultivars." Functional Plant Biology 38, no. 4 (2011): 337. http://dx.doi.org/10.1071/fp10171.
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In modern agriculture, the use of cultivars that are resistant against specific stresses, e.g. pathogen infections, is an integral component. Considering the great demand for a rapid and objective screening method for stress resistance of new cultivars, the question arises, whether time resolved fluorescence spectroscopy is suitable for such purposes. Amongst others, infected plants might accumulate specific compounds such as salicylic acid and phenylpropanoid compounds as key substances in plant disease resistance, whereas synthesis and accumulation may influence fluorescence parameters such as absolute intensity of single peaks, ratios between peaks and lifetime. Experiments were conducted in a controlled-environment cabinet cultivating four leaf rust susceptible and three leaf rust resistant genotypes. Fluorescence measurements were conducted using a compact fibre-optic fluorescence spectrometer with a nanosecond time-resolution. Results of experiments revealed that UV-induced measurements of spectral characteristics as well as determination of fluorescence lifetime are suited to detect leaf rust (Puccinia triticina) in wheat (Triticum aestivum L.) cultivars as early as 2 days after inoculation (dai). For this purpose several parameters such as the fluorescence (F) amplitude ratios F451/F522, F451/F687, F451/F736, F522/F687, F522/F736 as well as fluorescence mean lifetime especially at 470 nm, might be used. Discrimination between resistant and susceptible cultivars to the leaf rust pathogen could be accomplished 3 dai by using the ratio of fluorescence amplitude between the blue (F451 nm) and red (F687 nm) peak, and mean lifetime at 440, 500 and 530 nm. Our results indicate that the combination of spectrally and time-resolved fluorescence could be an additional tool in plant breeding programs for an automatic and precise high-throughput system for evaluation of the pathogen resistance of new genotypes.
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Kowalik, Michał, Michał Śliwiński, and Mateusz Papis. "Utilization of topology optimization and generative design for drone frame optimization." Aircraft Engineering and Aerospace Technology, April 4, 2025. https://doi.org/10.1108/aeat-12-2024-0384.
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Purpose This study presents the process of optimizing a DJI F450 quadcopter using two optimization methods: topology optimization and generative design. The goal was to improve the overall performance of the unmanned aerial vehicle (UAV) by altering its geometry and comparing the use of different materials. The purpose of this paper is to reduce the mass of the original DJI F450 frame by 10%. Design/methodology/approach To enable optimization using both methods, modifications were made to the original DJI F450 frame. A total of five cases were prepared: one for the original DJI F450 frame, three for topology optimization and one for generative design. Appropriate constraints were applied to simulate the real-world forces acting on the drone during flight. The various optimized frame designs underwent static analysis to evaluate stress, strain, displacement and safety factors. These results were then compared with those of the original DJI F450 frame, which also underwent static analysis. Findings The results revealed that the optimized DJI F450 frames could achieve improved performance under flight-like load conditions, with reduced mass and comparable stress and strain levels. This demonstrates that engineering optimization, combined with additive manufacturing, can yield superior results, producing lighter, more capable organic structures compared to traditional frames. Originality/value This research offers a novel perspective on optimizing UAV frames using topology optimization and generative design. The findings contribute to a deeper understanding of how engineering optimization, combined with additive manufacturing, can produce lighter, more capable structures for UAVs.
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Ahmed, Nigar, and Meng Joo Er. "Adaptive robust trajectory tracking control with states-estimation for DJI-F450 quadrotor under multiple unknown disturbances." Journal of Navigation, June 30, 2025, 1–21. https://doi.org/10.1017/s0373463325000189.
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Abstract A quadrotor unmanned aerial vehicle (UAV) must achieve desired flight missions despite internal uncertainties and external disturbances. This paper proposes an adaptive trajectory tracking control method that attenuates unknown uncertainties and disturbances. Although the quadrotor is underactuated, a fully actuated controller is designed using backstepping control. To avoid repeated derivatives of control inputs, a dynamic surface method introduces a filter and auxiliary controller. Lyapunov criteria guide adaptive laws for tuning controller gain and filters. A low-power observer is integrated for state estimation. Additionally, a disturbance observer is developed and combined with the control scheme to handle unknown disturbances. Simulations on a DJI F450 quadrotor demonstrate that the proposed control algorithm offers strong trajectory-tracking performance and system stability under multiple uncertainties and external disturbances during flight.
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Jemsa, Annis Syahirah, Syariful Syafiq Shamsudin, and Mohammad Fahmi Pairan. "Flight Control System Design and Dynamic Simulation for DJI F450 Quadcopter UAV Using Pole Placement Tuning Method." Progress in Aerospace and Aviation Technology 4, no. 2 (2024). https://doi.org/10.30880/paat.2024.04.02.003.
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Jasem, Ayad R., Sadiq Y. Muhammed, Abdulazeez N. Jameel, and Hussain M. Tuama. "Impact of strength exercises on the level of performance of male discus throwers of the F40 category." SPORT TK-Revista EuroAmericana de Ciencias del Deporte, January 31, 2022, 13. http://dx.doi.org/10.6018/sportk.509471.
Full textAbstract:
F40 category (short stature) athletes usually suffer from stability related problems. Due to these problems it is difficult for them to give their best performances, despite their regular strength training protocols. Hence, preparation and implementation of specific strength exercises might help them to perform better in discus throw. The present study aimed to find out the impact of specific strength exercises on the level of performance of male discus throwers of the F40 category. The atheltes from the Dhi Qar Committee constituted the population of the study. The sample was selected by purposive sampling method. A total of five participants were recruited of F40 category. In the present study, statistical significant results were obtained on comparison between pretest and posttest findings. The results of this study showed that the program of strength exercises applied significantly improved the level of performance of male discus throwers of the F40 category.