Difference between revisions of "R2P IMU firmware development"

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{{ProjectProposal
 
{{ProjectProposal
−
|title=R2P IMU firmware development
+
|title=Embedded Inertial Measurement Unit for Unmanned Aerial Vehihcles
 
|image=R2P_IMU.png
 
|image=R2P_IMU.png
−
|description=
+
|description=We have developed the electronics of an Inertial Measurement Unit based on an ARM microcontroller to be integrated on an autonomous embedded aerial platform. The IMU has already some attitude heading reference system (AHRS) code implemented, but we are interested in:
−
R2P (Rapid Robot Prototyping) is an open source modular architecture for rapid prototyping of robotic applications, which aims at speeding up their development.
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*implementing embedded algorithms for the estimation of the IMU attitude to be compared with the actual one (e.g., Kalman filter, DCM, Madgwick, etc.)
−
R2P is an open source modular hardware and software architecture, where off-the-shelf basic modules (e.g., sensors, actuators, controllers) are combined together in a plug-and-play way, with tools for an easy development of control software, allowing the implementation of a complex system in a simple and standardized way.
+
*developing a, easy to use, procedure for the calibration of IMU parameters
 +
*making a comparison with commercial units using a robot arm as testbed
 +
*validate the accuracy of the IMU on a flying platform
 +
*integrate the measurements from a GPS to reduce drift and provide accurate positiong (this will make it definitely a MS thesis)
  
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One of the modules integrated in the framework is an IMU (Inertial Measurement Unit), which exploits a 3-axis MEMS gyroscope and a 2-axis MEMS accelerometer to estimate its heading.
+
'''Material'''
−
A magnetometer is also present, in order to calculate absolute heading and correct the measurement from the other sensors.
+
*electronic board and eclipse based C development toolkit for ARM processors
 +
*papers describing the algorithms we are interested in implementing
  
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The project involves the study of a data filtering and sensor fusion algorithm and the implementation on an embedded microcontroller. The code must be written in ANSI C and optimized for the target embedded processor.
+
'''Expected outcome:'''
−
The board have been built and tested, and raw sensor data have been acquired to test its functionality. Scientific articles about data filtering and sensor fusion, and example implementation of the algorithms, are available.
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*few different AHRS algorithms with comparative results
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The only prerequisite is knowledge of the C programming language.
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*user-friendly procedure to calibrate the IMU
 +
*a sistem which integrated IMU and GPS to provide accurate positioning
  
−
|tutor=AndreaBonarini; MartinoMigliavacca;
+
'''Required skills or skills to be acquired:'''
−
|start=2012/03/01
+
*C programming on ARM microcontroller
 +
*background on kalman filtering and attitude estimation
 +
 
 +
|tutor=AndreaBonarini; MartinoMigliavacca; MatteoMatteucci
 +
|start=2015/01/01
 
|studmin=1
 
|studmin=1
 
|studmax=2
 
|studmax=2
 
|cfumin=2
 
|cfumin=2
−
|cfumax=5
+
|cfumax=20
 
|resarea=Robotics
 
|resarea=Robotics
 
|restopic=Robot development;
 
|restopic=Robot development;
 
|level=Bs; Ms
 
|level=Bs; Ms
−
|type=Course
+
|type=Course;Thesis
−
|status=Active
+
|status=Closed
 
}}
 
}}

Latest revision as of 16:25, 31 December 2014

Title: Embedded Inertial Measurement Unit for Unmanned Aerial Vehihcles
R2P IMU.png

Image:R2P_IMU.png

Description: We have developed the electronics of an Inertial Measurement Unit based on an ARM microcontroller to be integrated on an autonomous embedded aerial platform. The IMU has already some attitude heading reference system (AHRS) code implemented, but we are interested in:
  • implementing embedded algorithms for the estimation of the IMU attitude to be compared with the actual one (e.g., Kalman filter, DCM, Madgwick, etc.)
  • developing a, easy to use, procedure for the calibration of IMU parameters
  • making a comparison with commercial units using a robot arm as testbed
  • validate the accuracy of the IMU on a flying platform
  • integrate the measurements from a GPS to reduce drift and provide accurate positiong (this will make it definitely a MS thesis)

Material

  • electronic board and eclipse based C development toolkit for ARM processors
  • papers describing the algorithms we are interested in implementing

Expected outcome:

  • few different AHRS algorithms with comparative results
  • user-friendly procedure to calibrate the IMU
  • a sistem which integrated IMU and GPS to provide accurate positioning

Required skills or skills to be acquired:

  • C programming on ARM microcontroller
  • background on kalman filtering and attitude estimation
Tutor: AndreaBonarini (andrea.bonarini@polimi.it), MartinoMigliavacca (migliavacca@elet.polimi.it), MatteoMatteucci (matteo.matteucci@polimi.it)
Start: 2015/01/01
Students: 1 - 2
CFU: 2 - 20
Research Area: Robotics
Research Topic: Robot development
Level: Bs, Ms
Type: Course, Thesis
Status: Closed
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