how to make a remote control drone car
lets started
- 1.need a drone
- 2.need glue gun rod
- 3. four mini car wheels
instructions -
- 1.get a drone and lower side combine four wheels of the f car using glue gun rods
- 2. wait to ready and start your drone
pro
- easy to make
- it can fly and run
cons
- some investment
- not properly work on the road because it makes to fly
check drone your need for a drone car
conclusion
I hope you make your first drone car using simple methods if you have any queries please comment
and check my blog
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What Is a Drone?
DRONE DEFINITION: WHAT IS A DRONE?
A drone refers to any aerial vehicle that receives remote commands from a pilot or relies on software for autonomous flight. Many drones display features like cameras for collecting visual data and propellers for stabilizing their flight patterns. Sectors like videography, search, and rescue, agriculture, and transportation have adopted drone technology.Originally developed for the military and aerospace industries, drones have found their way into the mainstream because of the enhanced levels of safety and efficiency they bring. These robotic UAVs operate without a pilot on board and with different levels of autonomy.
A drone’s autonomy level can range from remotely piloted (a human controls its movements) to advanced autonomy, which means that it relies on a system of sensors and LiDAR detectors to calculate its movement.
A drone refers to any aerial vehicle that receives remote commands from a pilot or relies on software for autonomous flight. Many drones display features like cameras for collecting visual data and propellers for stabilizing their flight patterns. Sectors like videography, search, and rescue, agriculture, and transportation have adopted drone technology.
Originally developed for the military and aerospace industries, drones have found their way into the mainstream because of the enhanced levels of safety and efficiency they bring. These robotic UAVs operate without a pilot on board and with different levels of autonomy.
A drone’s autonomy level can range from remotely piloted (a human controls its movements) to advanced autonomy, which means that it relies on a system of sensors and LiDAR detectors to calculate its movement.
Abstract
A new aerial platform has risen recently for image acquisition, the Unmanned Aerial Vehicle (UAV). This article describes the technical specifications and configuration of a UAV used to capture remote images for early-season site-specific weed management (ESSWM). Image spatial and spectral properties required for weed seedling discrimination were also evaluated.
Two different sensors, a still visible camera, and a six-band multispectral camera, and three flight altitudes (30, 60, and 100 m) were tested over a naturally infested sunflower field. The main phases of the UAV workflow were the following: 1) mission planning, 2) UAV flight and image acquisition, and 3) image pre-processing. Three different aspects were needed to plan the route: flight area, camera specifications, and UAV tasks.
The pre-processing phase included the correct alignment of the six bands of the multispectral imagery and the orthorectification and mosaicking of the individual images captured in each flight. The image pixel size, the area covered by each image, and flight timing were very sensitive to flight altitude.
At a lower altitude, the UAV captured images of finer spatial resolution, although the number of images needed to cover the whole field may be a limiting factor due to the energy required for a greater flight length and computational requirements for the further mosaicking process. Spectral differences between weeds, crops, and bare soil were significant in the vegetation indices studied (Excess Green Index, Normalised Green-Red Difference Index, and Normalised Difference Vegetation Index), mainly at a 30 m altitude.
However, greater spectral separability was obtained between vegetation and bare soil with the index NDVI. These results suggest that an agreement among spectral and spatial resolutions is needed to optimizes the flight mission according to every agronomical objective as affected by the size of the smaller object to be discriminated (weed plants or weed patches).
A new aerial platform has risen recently for image acquisition, the Unmanned Aerial Vehicle (UAV). This article describes the technical specifications and configuration of a UAV used to capture remote images for early-season site-specific weed management (ESSWM). Image spatial and spectral properties required for weed seedling discrimination were also evaluated.
Two different sensors, a still visible camera, and a six-band multispectral camera, and three flight altitudes (30, 60, and 100 m) were tested over a naturally infested sunflower field. The main phases of the UAV workflow were the following: 1) mission planning, 2) UAV flight and image acquisition, and 3) image pre-processing. Three different aspects were needed to plan the route: flight area, camera specifications, and UAV tasks.
The pre-processing phase included the correct alignment of the six bands of the multispectral imagery and the orthorectification and mosaicking of the individual images captured in each flight. The image pixel size, the area covered by each image, and flight timing were very sensitive to flight altitude.
At a lower altitude, the UAV captured images of finer spatial resolution, although the number of images needed to cover the whole field may be a limiting factor due to the energy required for a greater flight length and computational requirements for the further mosaicking process. Spectral differences between weeds, crops, and bare soil were significant in the vegetation indices studied (Excess Green Index, Normalised Green-Red Difference Index, and Normalised Difference Vegetation Index), mainly at a 30 m altitude.
However, greater spectral separability was obtained between vegetation and bare soil with the index NDVI. These results suggest that an agreement among spectral and spatial resolutions is needed to optimizes the flight mission according to every agronomical objective as affected by the size of the smaller object to be discriminated (weed plants or weed patches).
