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DIY Drones Blog

How to Build a FPV Racing Quadcopter!

Thanks to Lumenier for providing parts for this build: www.getfpv.com After learning about the world of FPV quadcopter racing, we couldn't wait to build our own. With the help of Lumenier and FPV quadcopter flyer Charpu, we learn about all the components needed to build a solid mini racing quadcopter for under $850, FPV kit included. Charpu helps us assemble the quadcopter and gives useful tips for first-time builders. It's really not that difficult! Video shot and edited by Norman Chan Music 

ZTW Spider 30A OPTO ESC

 

Product Type   Spider 30A OPTO
Model Number   5030301
Type brushed/brushless brushless
  High Voltage / Normal Normal
Specifications Continuous Current 30A
  Burst Current 40A
  Battery Cell 5-18NC/2-6Lipo
  BEC output NO
Size Width 25mm
  Length 43mm
  Height 9mm
  Weight 25g
Wires & Connectors Power Wires 16#AWG,70mm-Red/70mm-Black
  Motor Wires 16#AWG,55mm-Black
  Signal Wires (Black & white twisted)30C,280mm a JR male plug at one side
  Power plug no
ESC Programing via Transmitter  
  AIR Program Card  
  LED Program Card  
  LCD Program Card  
  WiFi Module  
  USB program connector yes
Firmware Firmware Upgrade yes

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How to Build a FPV Racing Quadcopter!

Thanks to Lumenier for providing parts for this build: www.getfpv.com After learning about the world of FPV quadcopter racing, we couldn't wait to build our own. With the help of Lumenier and FPV quadcopter flyer Charpu, we learn about all the components needed to build a solid mini racing quadcopter for under $850, FPV kit included. Charpu helps us assemble the quadcopter and gives useful tips for first-time builders. It's really not that difficult! Video shot and edited by Norman Chan Music 

ZTW Spider 30A OPTO ESC

 

Product Type   Spider 30A OPTO
Model Number   5030301
Type brushed/brushless brushless
  High Voltage / Normal Normal
Specifications Continuous Current 30A
  Burst Current 40A
  Battery Cell 5-18NC/2-6Lipo
  BEC output NO
Size Width 25mm
  Length 43mm
  Height 9mm
  Weight 25g
Wires & Connectors Power Wires 16#AWG,70mm-Red/70mm-Black
  Motor Wires 16#AWG,55mm-Black
  Signal Wires (Black & white twisted)30C,280mm a JR male plug at one side
  Power plug no
ESC Programing via Transmitter  
  AIR Program Card  
  LED Program Card  
  LCD Program Card  
  WiFi Module  
  USB program connector yes
Firmware Firmware Upgrade yes

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Disturbance Rejection with Incremental Control of Accelerations


Wind Gusts
Drones have the potential to perform many useful tasks, such as search and rescue, package delivery and aerial imaging. But in order to perform these tasks in an outdoor environment, the vehicles need to be able to control their position under the influence of wind gusts. This is especially true when flying close to obstacles, as a position error due to a wind gust might result in a collision.

Incremental control
At the MAVLab of Delft University of Technology, we have taken the concept of Incremental Nonlinear Dynamic Inversion (INDI), and applied it to the linear accelerations of MAVs. The idea is that both disturbances as well as control forces are measured by the accelerometer. This means that a desired acceleration can be achieved by incrementing the previous control input based on the difference between desired and measured acceleration.

INDI can be compared to the integrator part of a PID controller, but where an integrator is blindly adding input, INDI takes the actuator effectiveness, actuator dynamics and filtering into account. This way, it can exactly determine the size of the input increment that should be applied, and it knows when the output should be expected. This allows the controller to react very fast to even the strongest of disturbances, such as a 10 m/s wind gust in the video above. (Note that the top speed of the Bebop is 13 m/s, according to Parrot!)

Wind tunnel experiment
In the experiment shown in the video, the drone is flying in and out of the wind tunnel flow, which is blowing at 10 m/s. We show a comparison of position control with INDI and PID, both with the same inner loop control. The position of the vehicle is sent to the drone at 4 Hz using an Optitrack infrared tracking system. When flying in and out of the wind tunnel, PID control leads to large overshoot, where it takes a long time for the integrator to compensate the change in wind. For INDI control, observe that the maximum deviation is much smaller, and that the vehicle returns to the correct position in a much shorter time.

The figure below shows the top view of the experiment, where the wind tunnel is blowing in the negative XN direction, and is located at −1.425 < YN < 1.425.

Autopilot

The use of INDI does not require any fancy sensors, except for the accelerometer that is standard on most drones. If the position loop is bypassed, it is even possible for a pilot to command accelerations; letting go of the stick will make the drone resist any acceleration, such that it keeps its velocity.

The INDI code is included in the Paparazzi open source autopilot, but should be easy to incorporate in other autopilots as well.


More information can be found in the paper, which can be downloaded from Elsevier (free for the first 50 days) or Researchgate.

Read Full Story

Disturbance Rejection with Incremental Control of Accelerations


Wind Gusts
Drones have the potential to perform many useful tasks, such as search and rescue, package delivery and aerial imaging. But in order to perform these tasks in an outdoor environment, the vehicles need to be able to control their position under the influence of wind gusts. This is especially true when flying close to obstacles, as a position error due to a wind gust might result in a collision.

Incremental control
At the MAVLab of Delft University of Technology, we have taken the concept of Incremental Nonlinear Dynamic Inversion (INDI), and applied it to the linear accelerations of MAVs. The idea is that both disturbances as well as control forces are measured by the accelerometer. This means that a desired acceleration can be achieved by incrementing the previous control input based on the difference between desired and measured acceleration.

INDI can be compared to the integrator part of a PID controller, but where an integrator is blindly adding input, INDI takes the actuator effectiveness, actuator dynamics and filtering into account. This way, it can exactly determine the size of the input increment that should be applied, and it knows when the output should be expected. This allows the controller to react very fast to even the strongest of disturbances, such as a 10 m/s wind gust in the video above. (Note that the top speed of the Bebop is 13 m/s, according to Parrot!)

Wind tunnel experiment
In the experiment shown in the video, the drone is flying in and out of the wind tunnel flow, which is blowing at 10 m/s. We show a comparison of position control with INDI and PID, both with the same inner loop control. The position of the vehicle is sent to the drone at 4 Hz using an Optitrack infrared tracking system. When flying in and out of the wind tunnel, PID control leads to large overshoot, where it takes a long time for the integrator to compensate the change in wind. For INDI control, observe that the maximum deviation is much smaller, and that the vehicle returns to the correct position in a much shorter time.

The figure below shows the top view of the experiment, where the wind tunnel is blowing in the negative XN direction, and is located at −1.425 < YN < 1.425.

Autopilot

The use of INDI does not require any fancy sensors, except for the accelerometer that is standard on most drones. If the position loop is bypassed, it is even possible for a pilot to command accelerations; letting go of the stick will make the drone resist any acceleration, such that it keeps its velocity.

The INDI code is included in the Paparazzi open source autopilot, but should be easy to incorporate in other autopilots as well.


More information can be found in the paper, which can be downloaded from Elsevier (free for the first 50 days) or Researchgate.

Read Full Story

Free Aerial Imagery Data (Personal/Commercial Use)

Free Aerial Imagery Data Collection JPGs, GeoTIFFs and Point Clouds!

We've updated our sample data page with a number of new drone based aerial imagery collections. These are available for personal and commercial use! We would love to hear about how you make use of these photogrammetry / drone mapping examples. There is a description of each data set located on the samples page and a combination of NADIR and Oblique collections. If you need a data set with Ground Control Points, contact us. Enjoy!

Thanks,

DroneMapper

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