====== Sensor fusion ====== ===== Overview ===== The aim of this project is to learn sensor fusion algorithms and implement them on ARM microcontroller. ==== GY-80 ==== GY-80 is a cheap sensor board. Available on Ebay and DealExtreme. \\ {{:projects:gy-80.jpg?350|http://dx.com/p/gy-80-bmp085-9-axis-magnetic-acceleration-gyroscope-module-for-arduino-145912}} Board feature 4 sensors providing in total 10-dimensional information. ^ Sensor ^ Description ^ I2C Address (8bit) ^ | [[http://www.st.com/st-web-ui/static/active/en/resource/technical/document/datasheet/CD00265057.pdf|L3G4200D]] | ST three-axis digital output gyroscope | 0x69 | | [[http://www.analog.com/static/imported-files/data_sheets/ADXL345.pdf|ADXL345]] | Analog.com 3axis Digital Accelerometer | 0x53 | | [[http://www51.honeywell.com/aero/common/documents/myaerospacecatalog-documents/Defense_Brochures-documents/HMC5883L_3-Axis_Digital_Compass_IC.pdf|HMC5883L]] | Honeywell Three-Axis Digital Compass | 0x1E | | [[http://www.bosch-sensortec.com/content/language1/downloads/BST-BMP085-DS000-05.pdf|BMP085]] | Bosh Digital Pressure Sensor | 0x77 | ===== Project assumptions ===== The purpose of this project is to provide sensor fusion solution using low-cost sensor board. ===== Phase I ===== * Build a I2C ↔ PClink * Set up sensors * Read data periodically * Plot the data image/svg+xml GY-80 StellarisLaunchpad SPI PC ==== Launchpad connections ==== * Serial transmission parameters: ''115200 8N1'' | 1.1 | GY-80 VCC_3.3V | | 1.10 | GY-80 SDA | | 1.9 | GY-80 SCL | | GND | GY-80 GND | ==== Source code ==== * [[:projects:gy-80:Processing_GUI]] ==== Screenshots ==== {{:projects:gy-80_plotter.png?direct|}} Notes: * Scaling is being adjusted in real time, shake sensor board to set maximum values so graph can be scaled to fit the window. * Keys ''1'' to ''-'' turn on/off plotting value * Key ''c'' clears the window ===== Phase II ===== * Implement 1D Kalman filter ==== Screenshots ==== {{:projects:gy-80_kalman.png?direct|}} Notes: * Key ''k'' - toggles filtered graph * Key ''m'' - toggles measured value ==== Source code ==== * ARM code unchanged * [[:projects:gy-80:Processing_GUI#kalman]] ===== Phase III ===== * 2D Sensor fusion Following graph show angle measurement using accelerometer (red) and gyroscope (blue). Gyro clearly shows error-induced drift. {{:projects:gy-80_drift.png?direct|}} ==== Complementary filter ==== image/svg+xml Accelerometer Gyroscope Low Pass filter Integration ∫ High Pass filter Combine Σ Angle Complementary filter is a good alternative for small systems. http://web.mit.edu/scolton/www/filter.pdf ==== Comparison ==== {{:projects:gy-80_kalmanvscomplf.png?direct|}} Filters: * Red - Complementary * Green - Kalman While both methods provided unbiased value, the Kalman filter provided more stable readout. Present-day MCUs provide sufficient power to use Kalman filter in real-time. ===== Phase IV ===== * Build balancing robot. {{http://www.youtube.com/v/CwFBXYt4UKg?.swf?420×315}} The robot consists of 5 parts: Tamiya gearbox, double H-bridge driver, Bluetooth wireless module, Stellaris launchpad board and GY-80 sensor board. The power is provided externally. GY-80 board provides accelerometer and gyro sensor measurement at 100 [Hz] (UPS variable). Sensor data is then processed by kalman filter and feed into PI controller. Control signal is driving PWM output driving motors H bridges. Notes: * The stability is good but not perfect * Kalman filter response was rally bad. I have boosted the response by multiplying angular acceleration value. Estimated angle value has overshot now but is fast enough. * Robot motors are powered externally. Wires are influencing robot stability. * Taller robot would be much better (bigger moment of inertia). * Both robot wheels are independent. Connected wheels would work much better reducing yaw. {{:projects:gy-80_balancer.zip|Source code}} {{:projects:gy-80_balancer.png?nolink|}} Robot operation. Red - accelerometer angle. Green - Gyro angular acceleration. Blue - estimated angle. Yellow - PI control signal. ===== References ===== * www.processing.org * [[http://www.ti.com/tool/ek-lm4f120xl|TI Stellaris Launchpad]] * [[http://www.cs.unc.edu/~tracker/ref/s2001/kalman/index.html|An Introduction to the Kalman Filter]] - SIGGRAPH paper by Greg Welch and Gary Bishop * [[http://web.mit.edu/scolton/www/filter.pdf|The Balance Filter]] - MIT presentation by Shane Colton * [[http://blog.tkjelectronics.dk/2012/09/a-practical-approach-to-kalman-filter-and-how-to-implement-it/|tkjelectronics blog]] - A practical approach to Kalman filter and how to implement it * [[wp>kalman_filter|Kalman filter, wikipedia]]