What’s this About
I’m rebuilding The Groundhog to a more professional level, with the level of accuracy required for the AI and computer vision work planned. It’s also getting an upgrade to the avionics to make it more resilient. This post details the rebuild and also has links to the 3D printed parts used.
Continue reading “Pixhawk 2 with Jetson TX2 Build”
22 students from France will be spending the next two weeks building and coding autonomous drones as part of the UWE Bristol Summer School. Along with Miles Isted s’Jacob, I am delighted to be leading on this activity and have produced a short sneak peek video of the challenge to share.
So six team drones racing autonomously on a single track? What’s not to like?
Code is based on that used for MAAXX Europe, so Python Dronekit, with ArduCopter on Pixhawk. However, the final code will be posted on my github at the end of the Summer School.
If you have heard of Robot Operating System and want to use it to monitor and control UAV flight, this post will get you started…
More specifically, this post details how to set up a Pixhawk flight controller running PX4 firmware, with a Raspberry Pi3 companion computer running Robot Operating System. This combination will give flexible control over the flight control unit and the ability to integrate a very wide range of features such as depth-sensing cameras and machine learning networks.
Continue reading “Robot Operating System for Flight Monitoring and Control – Getting Started.”
This is part of a series of posts outlining the evolution of my GroundHog hexacopter into a multi-role UAV. It is based on a Pixhawk flight controller with a Jetson TX2 companion computer. It has now been fitted with an Intel RealSense D435 depthcam.
Continue reading “First Flight: Intel RealSense D435 Depth Camera on Jetson TX2”
Part of a series of videos and blogs tracking the development of The Groundhog, which was entered into the MAAXX Europe 2017 competition earlier this year.
Having successfully tested the re-written code to follow straight lines using velocity vectors for control and NED space mapping for line detection, we test it around a 50m track comprising 50mm wide red webbing – and we speed it up a bit as well.
The test turned out to be quite successful, with following speeds of 1.5m/s achieved under autonomous control provided by an on-board Raspberry Pi 3. This is significantly faster than the winning UAV in MAAXX Europe this year, which is quite pleasing!
The YouTube video shows both on-board and off-board camera footage, the former demonstrating the roaming regions of interest used by OpenCV to maintain a lock under varying lighting conditions.
Continue reading “Groundhog UAV curved line following”