This video illustrates the adaptation of a ArduPilot Rover to the Artificial Intelligence DonkeyCar environment. This includes the adding-editing of 5 files of the DonkeyCar library. A linear AI model trained by behavioral cloning resulted in a satisfactory autonomous test drive.
By using Intels OpenVINO and neural computing stick (NCS1) the inference speed of my neural network is increased from 5 frames per second (fps) to 20 using a RPI3. This enabled a far better obstacle avoidance performance of my drone. More info at https://github.com/avncalst/drone_cnn.
Simple testflight of my E-Flite Convergence equipped with a pixracer flight controller complemented with a "sbus to pwm" home-made circuit to extend the number of servo outputs. Flight stack: Ardupilot Quadplane; Flight mode: loiter.
Added by andre van calster on July 2, 2019 at 9:00am — No Comments
By changing the CNN of my previous post CNN (1) into a multi-classification CNN (fly, go left, go right, stop) and adding an appropriate multi-thread python dronekit script, my drone…Continue
Recently I started working on convolutional neural networks (cnn) for obstacle avoidance. With the help of "DroNet" from ETH Zurich and the "Deep Learning for Computer Vision" book from Adrian Rosebrock, I managed to build my first cnn algorithm for obstacle avoidance. The cnn module is written in python, using a keras…Continue
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This winter I rebuilt my XJ470: added a PiZero with a Picam. This PiZero functions as a companion computer with a python dronekit and a WiFi access point enabling a mavlinkbridge. Hence an android tablet, running Tower, can be connected to this WiFi access point for…Continue
For bench marking my own developed flight controller, I built a Quanum XJ470 drone with a DroPix (Arducopter/PixHawk) flight controller. Some additional features: esc AFRO 30A; motors: SunnySky 980 KV; props 10 x 4.5. My first testflight shows that the drone with the DroPix flies great.
Complementary filters enable sensor fusion, are easy to implement and eliminate integrating errors from gyro’s or accelerometers. Normally complementary filters are discussed in the frequency domain. Using simple Python code a 1st order linear complementary filter for determining the quadcopter ground speed was analyzed in the time domain. The results are…Continue
I upgraded my 3D printed crossfire with a Boscam transmitter TS351 and a all-in-one monitor RX-LCD5802. The FPV camera is a GoPro Hero 3. Simultaneously a Sparkfun GPS LS23060 was added. This implied adding code for reading, parsing and processing nmea data. I have chosen a complementary filter approach for fusion of accelerometer data and GPS data. I managed to…Continue
After tuning my PID parameters for both my 3D printed quadcopters crossfire and miniflame, I succeeded in having some decent test-flights. The flight controller used is an own built controller inspired by KK2, AeroQuad and ArduCopter. Future development: including a GPS module.
Added by andre van calster on October 6, 2014 at 3:38am — No Comments
Having a background in electronics I wanted to build my own flightcontroller. At the same time I decided to build a new quadcopter. Having bought a 3d printer from Ultimaker, I printed a quadcopter frame inspired on the original Crossfire design from MickeyB (thingiverse).