The ArduCopter Evolution Team is opened to all the pilots who want to explore, develop and test new features added to the official release of the ArduCopter firmware.
Anyone who wants to share freely, with an open mind, some ideas and software improvements for the ArduCopter firmware is welcome here.
JLN
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http://nn-os.org/ provides a new computing environment for Neural Network Control applications of all sorts. Essentially the environment is comprised of two components:1. Neural Network adaptive learning, currently back-propagation2. Petri Nets with BSD sockets to orchestrate asynchronous multi-tasking and IO across the networks For examples see:http://nn-os.org/nnconductor/ http://nn-os.org/petri-net-nnnet2/http://nn-os.org/petri-net-nnnet2-2/ The software is optimized using the…
Read more…hi Arducopter fans there,i will try to give some suggestions regarding arducopter v2.6 before its release. Since there has been some time without any improvement about AHRs algorithms over recent arducopter versions. I think, the best place to begin is DCM algoritm itself.My suggestion is to combine DCM and quaternion methods with their most superior parts. I know there has been a marg quaternion algorithm, but it is unnecessarily complicated. We know that DCM is easy to understand and has a…
Read more…by J. Saranpagani Taylor and Francis GroupAn encyclopedia like list of all known NN applications to control systems all the way from 1940s to now with MATLAB and some C code. All the math explicated with explicit calculations so the coding can be more educated rather than just copying. I will type up all the equations and algorithms, for people who do not have the book.The control systems are from actual machinery used in different industries and all the relevant research papers listed. My hope…
Read more…I got this generous input from one of the XFOIL's usergroup. These ideas give a better simulation for the copter than the one's I have hacked already since the suggested tools are more mature and worked on by the experts in the field. Amazing thing I recognized this week was that the complicated rotor crafts can be off-line trained by NN for complex flying i.e. the customer purchases a copter and downloads the NN offline trained weight matrices for immediate flight e.g. the novice fliers will…
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During the 3rd quarter of 2011, as a student, I have successfully followed the first online Machine Learning course teached by Prof Andrew Ng from the Stanford University. This was a very intense and exciting adventure and I have greatly improved my experience in this field of knowledge. I am very grateful to Prof Andrew Ng and his Machine Learning team from the Standford University.
Jean-Louis Naudin
Thanks DARPA we accept technology transfer from:
Autonomous Autorotation of an RC Helicopter
Pieter Abbeel1, Adam Coates1, Timothy Hunter2, and Andrew Y. Ng1 1Computer Science Department, Stanford University, Stanford CA 94305
2Electrical Engineering Department, Stanford University, Stanford CA 94305
We thank Garett Oku for piloting and building our helicopter. This work was sup- ported in part by the DARPA Learning Locomotion program under contract number FA8650-05-C-7261. Adam Coates is supported by a Stanford Graduate Fellowship
I attempted Dr. Ng's Machine Learning course while also getting certification training for MS SharePoint 2010 Admin & Infrastructure design,, way too much overload... So I backed off machine learning, but soon enrolled in Autonomous Vehicle course. I have ST32F4 from Element 14 on its way, It maybe able to handle some of his AI?
Andrews team is amazing, Only a handful of pilots can fly as well as his AI
Testing ACM 2.4 Heli for legacy Raptor with my secret sauce..
Remzibi OSD with Call_Sign every two minuets,
Stabilized camera control with operator nudge (3-way switch controls mix mode to Ch 5 roll & Ch 6 tilt.
Controlled with TG-9x with FrSky TM module, &
3D/DGPS lock for Ublox.
Installed Atolic Deveopment Lite waiting for ST324F4...
Chopping at the bit... while I debug missing #if statement.