Autonomous UAV Coding Summer School – UWE Bristol

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.

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Robot Operating System for Flight Monitoring and Control – Getting Started.

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.”

First Flight: Intel RealSense D435 Depth Camera on Jetson TX2

 

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”

Intel Realsense D435 on Jetson TX2

JetsonTX2withD435

Here’s a quick technical post for anyone attempting to harness the capabilities of a Realsense D435 camera on a Jetson TX2.  For me, this is about getting usable depth perception on a UAV, but it has proved more problematic than I originally anticipated.

This post aims to provide some simple instructions that now work for me, but took a long time to find out!

The Problem

The Intel librealsense2 library does not support ARM architectures as I write.  This causes a fatal compile error when the file librealsense/src/image.cpp is accessed, as it queries the system architecture.

Solution

Modify image.cpp as in my Github gist here.  This bypasses the architecture check.

Installation Sequence

  • Flash the TX2 with JetPack 3.2
  • git clone https://github.com/IntelRealSense/librealsense.git
  • cd librealsense
  • sudo apt-get update
  • modify librealsense/src/image.cpp as per gist above
  • sudo apt-get install git cmake
  • sudo apt-get install libssl-dev
  • sudo apt-get install libusb-1.0-0-dev pkg-config libgtk-3-dev
  • sudo apt-get install libglfw3-dev
  • sudo apt-get install libudev-dev
  • sudo apt-get install cmake-curses-gui
  • sudo cp config/99-realsense-libusb.rules /etc/udev/rules.d/
  • sudo udevadm control –reload-rules && udevadm trigger
  • reboot
  • mkdir build && mkdir install
  • cd build
  • cmake ../ -DBUILD_EXAMPLES=true -DCMAKE_BUILD_TYPE=release -DBUILD_UNIT_TESTS=false
  • make -j4
  • sudo make install
  • Connect D435 to the USB3 port
  • Navigate to the tools at usr/local/bin, such as:
    • ./realsense-viewer

Issues

I am getting some warnings of incomplete frames, but it’s not clear if this is due to a power issue on the powered hub or a software configuration.  Despite this, the provided tools seem to work well and demonstrates some of the best depth camera capabilities I have seen (and this is my third depth camera to date).

MAAXX Europe 2018 DroneJam Coding MasterClass

DSC_0118

(Nb. All resources for this post can be found on GitHub at https://github.com/mikeisted/maaxxeurope2018)

It’s been a while since my last post.  My research has since moved towards the use of machine learning in UAVs and so my trusty Groundhog now sports a Jetson TX2 instead of a Raspberry Pi and an Intel Realsense depthcam for ‘deep vision’ to match it’s deep learning capabilities.  But I digress… so I’ll blog more on this another time…

This post is about the DroneJam coding masterclass for autonomous UAV ‘newbies’ I ran for this year’s MAAXX Europe autonomous drone competition, held in March at the University of the West of England.

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Groundhog Autonomous UAV – Going faster with sunglasses

The Groundhog is being developed to line-follow at low altitude and higher speeds.  This video is of the field testing taking the speed up to 1.75 m/s.  It also explains why the Groundhog now sports sunglasses.

The Track

An oval of 50mm red webbing, bends of radius approx 3m and straights of approx 15m. Testing took place in early morning with glancing sunlight on dew-soaked webbing – great for walking the dog but not so good for computer-vision.

Continue reading “Groundhog Autonomous UAV – Going faster with sunglasses”

Groundhog UAV curved line following

groundhogcurve
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”