Skip to main content

The obvious distraction to drone flight control research - Aerial videography



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 not just flight log data but aerial footage? What kind of quality footage will it be if I'm doing aggressive maneuvers and system identification procedures. 

The one thing that I know will be beneficial is to record the rotor dynamics as the system identification process is done and how the intelligent controller takes that into account. But this does not require a gimbal as the camera will be positioned just about the rotor hub throughout the flight mission.

So whichever angles (and I'm sure there's many more) to look at it, the integration of gimbal and camera equipment on a drone used for flight control research is not aligned and could actually be called a distraction to the focus of the research and a potential enticement to flying the drone in undesirable conditions resulting in unforeseen and catastrophic crashes.

I'm sticking to my principle: Keeping it simple and keeping it focused.

Comments

  1. Nice post. I'm impressed by this blog. There is so much information about this topic. Thanks for sharing an amazing article like this. please keep posting. Dubai Aerial Videography

    ReplyDelete

Post a Comment

Popular posts from this blog

Setting up the Tarot T4-3D gimbal on the Pixhawk 2.4.8 with Specktrum dx6 Gen2 toggle switch

So i took the challenge of setting up the Tarot gimbal not just for inherent stable video footage but also the flexibility of controlling it from the radio control. However, I encountered quite a few challenges which made me aware that I'm not the one only in this battle . It's quite clear that the setup of the Tarot gimbal using its own software is completely different from how it's been described in the Ardupilot/Arducopter webpage and in mission Planner. In Mission Planner and it's associated site makes one believe that it should be done through software, only to realize that in actual fact the setup is more complex than that.  After two evenings of trying various combinations, I realized the getting the pixhawk Aux channels to communicate with the T4 gimbal requires the following steps: - The Pixhawk Pin9 (Aux1) needed to be activated to pass through user-chosen channel from the transmitter. For the Dx6 Gen2 it was the channel 6, which can assigned the

Matlab to C/C++ code development - Some learning points

Over the last few years, the engineers at the company have invested both their time and sleepless nights in formulating a process for the development of Machine learning algorithms that will satisfy real-time constraints with minimal RAM usage. This is quite a tall task as per default, that would force one to do their development directly in C language. Although that seems like the right choice, the downside is the direct correlation of the debugging time with algorithm complexity.  Such a time could have been rather used in optimizing the algorithm within the MATLAB environment which has excellent tools for the analysis, plotting and debugging. So it was decided to rather learn the Code generation process with the hope that future algorithm could be designed in a similar fashion without the hassle of the compiler-specific run-time issues. The development of this machine learning algorithm would eventually be implemented in a 32bit, 160Mhz speed, 260KB RAM microcontroller.

The hard climb of innovation

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