Academic literature on the topic 'DJI F450'

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.

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.

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.

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.