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UWB Local Positioning System

RF Ranging tags and an iterative least squared estimation to solve for a 3d position in a room

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In an attempt to try and fly a drone indoors, we worked (and succeeded) on building an indoor positioning system. We used 2 sets of TREK1000 demo kits from DECAWAVE to calculate ranges from each of the anchors to our tag on the robot. We then used an iterative least squared estimation (think linear algebra and GPS) to solve for the postion which then was broadcasted via XBEE.
The eventual goal is to use migrate to the DWM1000 (much cheaper) and using kalman filters and LIDARs/ Barometers to augment the indoor postioning system

Final Iimplementation of postioning code(python) and arduino code we used on the RC Car to navigate in a room availible -> https://github.com/julianblanco/uwbLocalPosnSystem
  • 8 × Trek1000 UWB RF Ranging
  • 1 × BeagleBone Used to run the python script and send data via xbee
  • 5 × Xbee Series 2
  • 8 × 7805 Linear Regulator
  • 7 × Lipo batteries

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RigTig wrote 05/12/2017 at 10:40 point

You might find some useful additions to your approach from work being done for a 3D printer: see the log https://hackaday.io/project/13420/log/56335-calibration-with-multilateration-using-w1209, and the associated comments.

I am not sure which approach might be quicker, 4 points giving squares or 5 points giving linear. I'll have to check how you work with just 2 fixed reference points, but I read somewhere that even one is enough. It is just the number of sample points that finally gives the location.

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