For the last couple of months, our design team has been hard at work at detail development of our drone concept which we hope to make public early 2021. These have been unprecedented times with so many changes within our company: people moving countries, stuck at airports, universities closing and transitioning to online classes and exams; all in the space of one year! Nevertheless, one of the fundamental challenges facing the drone industry in developing countries next year, is how to operate within an environment where shipping of critical parts (amongst other things) has been disrupted due to the covid-19 pandemic. If the most critical items (propellers, batteries, sensors, etc. ) of the system are also associated with the longest lead time, this has a significant impact on the operating cost and service coverage that can be achieved. Unfortunately, there's no easy way of solving this issue except if it was envisioned as part of the development process. But this is seldom the ca...

The objective for this milestone was to integrate the same model functionality developed and analyzed within the Matlab/Simulink environment into a mature environment that will be able to test most functionalities of the Flight controller software that will be flashed for real-flight testing. The decision was to either migrate the Ardupilot (in this case ArduCopter ) software into the Matlab environment or integrate the highly nonlinear quadcopter model with flapping dynamics into the Ardupilot environment. The former option would mean no easy integration with Mission Planner and the real-time sofware-in-the-loop ( SITL ) testing tool (which also includes the infrastructure to communicate with the Flightgear 3D visualization environemt, while the later with make use of singular environment although the software development effort would quite tedious and error-prone. It was chosen to go with the first option as this was thought to be lead to more mature verification method prior...

Our H1 drone was finally ready to be tested with new detachable landing gear, 3-axis gimbal and the Gopro camera (powered by an extra 3S-lipo battery). The objective of the test was to test the flight modes (mainly altitude control and position control) via 3DR telemetry connection to an Android phone and the Tower app. It was quite noticeable that the quad needs alot more power to overcome in-ground effects at take-off. This is mainly due to the mass balance with the camera at the nose of the airframe. The altitude control mode worked quite well even at low elevation of 3-4 metres. Unfortunately, at low-battery voltage an automated landing doesn't occur and this resulted in hard-landing (more like a hard crash!). This piece of software will have to be investigated. The manual operation of the Gopro is a bit cumbersome. The alternative of using the WIFI link to operate the camera was investigated but was not deemed a good idea given the potential inteference...

We stumbled on the organization that's making head waves around the globe called Drones for Good Award . Although the coveted prize money is not openly advertised, various companies have participated over the past few years such as: PrecisionHawk , LoonCopter , Drones against Tsetse and many more. We were pretty impressed especially with LoonCopter. This is drone which is capable of air, surface and underwater navigation. The aim is mainly for search and rescue and the proof of concept was demonstrated at the award. But what's more fascinating is the culture and ethos this organization promotes. The idea that drones CAN be used for good and SHOULD be used for good. This is something we at Uav4africa believe immensely . The notion that you can use technology, whether in the air or under the sea, to uplift, educate and empower underprivileged communities is beyond a nice gesture, it's calling all of us should respond to. One of our projects is to investigate...

So the notion of upgrading my already awesome (if I can say so myself) looking drone to aerial videography using a 3-axis gimbal has been bugging me for a while now. I mean, why not? At least that will get me to fly the drone alot more and use it for other purposes. The fact that I only need a gimbal and a landing gear (given that I already have the awesome Gopro hero 4 silver), should be providential enough to just spend the dollars required to make this happen. But then one get's to think, why I am doing it for? I mean does my research of intelligent flight control ACTUALLY need aerial capability? One could argue that testing your software with a drone representative of an actual commercial drone could only enhance the validation/justification of the research.  But the ultimate question is, how MUCH distraction will this capability introduce to the essence of what the doctoral research is trying to achieve? Will I gain more information given that I've got now no...

So one of the main objectives of my PhD research was to achieve the difficult task of developing a learning algorithm for machine learning ( RBF neural networks to be more specific) applications, that would enable the prediction of drone propeller damage in real-time AND without altering the bought-out flight controller ( DJI Naza , APM , Pixhawk , etc...). The only way it could achieve that was by analyzing the outputs of the flight controller sensors and learn when a fault would occur. Well, I believe I'm getting closer to this objective (submission is Nov 2019). I've decided to include the two figures below which illustrates the training process (0.2 sec on desktop) and prediction time (0.008 sec) and the accuracy to the true dynamics of the quadcopter drone. In this case the pitch dynamics are being predicted. Although noise hasn't been introduced, it's quite clear from the graphs, that the learning algorithm has enabled the RBF network to accurately capture th...

So my pixhawk has finally arrived! Now it's time to get to understand the codebase and integrate my algorithms to the flight control suite. I've also gathered a few parts from a research quadcopter. The plan is to have it takeoff and demonstrate that it works!

It's amazing how a continuous search at possibilities eventually leads to finding a "needle in a haystack". Introducing the Teensy 3.6 development. It a 32-bit Cortex M4 ARM core with (FPU) at a 1/6th of the price of the Pixhawk (The Pixhawk runs the same chip although has double the Flash memory). Granted, it doesn't have any sensors but man, that's find. Did I also mention that it works straight with arduino code. This means that I've upgraded to the most powerful MCU in the market at the same price as an Arduino Due . Wow! I overcame my shock by going to Robotics in Centurion and getting my hands on this dynamite. It can even run X-plane flight simulation controls in real-time communicating through the USB port! Ok enough ranting and raving! The point is now I can test the embedded algorithms on a similar ARM-based microcontroller as the Pixhawk and conclude the testing and validation up to HILS level with the sensors in the loop (while emulating th...

When will I fly again? I've been asking that question for a while now. After that expensive and silly mistake of loosing my Blade 300 CFX to some uncalibrated mass balancing, the wind of excitement was whiffed out of my spirit. I must say, this has allowed me to refocus my energies on the helicopter simulation model and how it should be modeled such that the research of the neural network algorithms could be easily prototyped and embedded into an autopilot software suite for HILS and flight testing. I must say, I feel like I've made quite a bit of progress on this front. Even though my spirit is itching to get something flying in the sky, the notion that I'm getting closer to creating a simulation environment where these algorithms can be tested, explored and refined for flight testing. I'm also grateful that during this time, my company has agreed to fund my research and I now the ability to procure the items that I need to properly get this research off the grou...

It was time to decide. Playing small robots and chiefs with the likes of Arduino and the soldering iron was a nice learning curve but it had to come to an end. The bigger objective of this research was to integrate intelligent algorithm on a platform that was accepted by most engineers and hobbyists. The learning pain will be great, but the support community will be there to help. The need to do things properly and start from a good foundation given the experimental nature of this research is key. It's clear there will be limitations, but what's obvious is that whatever software I build will be implemented on an architecture that's continously changing and being upgraded due to the fast changing nature of current drone industry. So the sooner I get into this game properly, the better it will be for future algorithms. Given the function of software in the loop, implementing a whole range of algorithm will now become a breeze. The prospect of using cheaper materials to...