TdxCopter
Final Submission: 01-Jul-2014
The main goal is to develop our own control algorithm. The Colibri module directly connects to the sensors and handles the motors. We hope to achieve a very stable system and superior flight characteristics due to direct control of the motors and using the powerful microcontroller Tegra 2 ARM Cortex A9.
Project details
Simulating, Programming and Construction of an autonomous Quadcopter with Toradex components.
Motivation
We want to be part of this aerial revolution and build a quadcopter on our own. To understand as much of the technical capabilities of the system as possible, we will develop the control algorithm rather than using an off-the shelf controller. This presents a hard challenge, as we will need control theory far beyond our educational background.Quadcopters have gotten very popular in the past few years. They are used as toys, surveillance systems, for photography, rescue missions and even music choreographies [1]. The graph to the right shows the search queries for quadcopters on Google. We can infer that these flying machines will become even more important in the future and be readily available for all sorts of applications.
We hope we can achieve a very stable system and superior flight characteristics due to direct control of the motors and using the powerful microcontroller Tegra 2 ARM Cortex A9. We are eager to tackle any challenges that are waiting for us and looking forward to testing some interesting maneuvers and applications as soon as our TdxCopter is up in the air!
Project Goal
The goal of this project is to build a flying quadcopter with the Colibri T20 as control unit. We will use standard components from the RC-branch as well as sensors used in consumer electronics. To achieve this goal we defined a list of steps below that we will work on during the challenge.
| Phase 1 Preparations |
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| Phase 2 Flying |
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| Phase 3 Applications |
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System Overview
As a starting point we use the Talon V2 carbon frame [3] and self-designed mounting plates for the Colibri Module and electronics. The quadcopter consist of many different components. We tried to summarize the whole setup in the following graphics.
Fig. 2: Quadcopter Hardware Overview with sensors and equipment.
The core is depicted by the Colibri T20 module. It has connections to all surrounding components. It is mounted on the Iris carrier board which provides us with various interfaces for communication. Very roughly speaking, the Colibri needs to read data from all sensors every few milliseconds. Thereafter the readings need to be filtered and combined to estimate the position and attitude of the quadcopter. In a last step the motors need to be addressed to correct deviations in attitude and position from the desired setpoints. To get information to and from the quadcopter we established wireless connections with XBee and UDP which let us communicate through our graphical user interface on the PC. Last but not least we attached a camera to the frame for taking images and videos during flight. All details about the quadcopter are found in the update pages below together with lots of impressions of the problems we faced and our methodology.
Social Media
Feel free to visit our website, youtube channel and facebook page. If you have questions or feedback of any kind let us know in the comments below. You can also contact us by directly by email. We are enjoying the various kinds of feedback we get from the community and are always happy to discuss some technical problems with fellow students and hobbyists!
Email: Contact Team TdxCopter
YouTube: TdxCopter Channel
Google+: G+ page
Facebook: TdxCopter fb
We will post updates on our project frequently, so have a look from time to time!
DOWNLOAD SOURCE CODE HERE: http://www.tdxcopter.com/file-cabinet
References
[1] Music in Motion, http://www.idsc.ethz.ch/Research_DAndrea/Music_In_Motion_2, 22.09.2013.
[2] Gootle Trends, http://www.google.com/trends/explore?q=quadrotor#q=quadcopter&cmpt=q, 22.09.2013.
[3] Talon Qc Frame, http://www.hobbyking.com/hobbyking/store/__22397_ , 22.09.13.
[4] “Wer misst misst Mist”, freely translated: “Someone who measures measures nonsense”, reminding us that every measurement has errors to it.
[5] Triple-Axis Digital-Output Gyro ITG-3200, https://www.sparkfun.com/products/9801, 23.09.2013.
Updates Hide
17 Oct 2013 03:33 pm
Hi! how do you think to sync the data coming from accelerometer and gyro in order to process it in the data fusion algorithm?
21 Oct 2013 03:59 am
Our current approach is to sample the data at a higher rate than the control loop is running in order to use basic filters to get more accurate values. The gyro and acc. might not need direct synchronization since the acc. plays not a vital role in the short-term control of the quad. Nevertheless we need it to minimize the integration errors for better angle estimations.
21 Oct 2013 04:00 am
We will post an update on this matter when we start working on our IMU-algorithms, as we do not know yet which problems we will face.
PS: Your SEASTICK project is awesome!
22 Oct 2013 07:22 am
Thanks, yours is very interesting too! i'm just asking because i already faced to this problem developing the IMU of the seastick. My idea is that to guess the angle you need to integrate the gyro, and even if you minimize the error, you will continuously integrate it... early or late it will diverge more and more . Take a look at extended Kalman Filter o DCM algorithm. bye, Flavio