Robot hardware

• cortex m4f mcu, 72MHz, stm32f303
• motor driver TI drv8834
• pololu HP motors 1:30 with magnetic encoders
• pololu 28mm tyres, high adhesion
• IMU (gyro + accelerometer) LSM6DS0
• 8 line sensors sensitive for green light (540nm), 3 IR obstacle sensors
• 180mAh LiPol 2S acu
• USB programming via bootloader

opengl debug application

I created user friendly debug application, showing :

• line sensor raw data
• motors status - speed, encoders
• IMU
• neural network classification process
• raw data from USART

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Robot software is based on this key points :

• quadratic interpolation  : high precission line position computing
• main loop running on 200Hz
• steering PID : PD controller for steering
• motor PID  : two PIDs for motors speed controll
• curve shape prediction : run on straight line, brake on curve; deep neural network
• written in C++
• network training on GPU - own framework for CNN

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Neural network for line shape prediction

• fast run on straight line, brake on curve
• neural network for line type classification, DenseNet - densely connected convolutional neural network
• input 8x8 matrix raw data from line sensors, 8 past line positions from 8 sensors
• output 5 curves types (two right, two left, one straight

Line prediction idea from last 8 line positions is on following figure

Line dataset was made as artificially generated data,

• 25000 for training
• 5000 for testing
• augmentation - luma noise, white noise

Network architecture
I was fighting with robustness against network speed. Network have to run 200Hz.
I decide to use DenseNet, which is very computation effective - in layers you need less kernels than in pure CNN.

Final network architecture is :

which is able to achieve 95% classification accuracy

layer	layer type	input feature map tensor size
0	convolution 3x3x4	8x8x1
1	max pooling 2x2	8x8x4
2	dense convolution 3x3x4	4x4x4
3	dense convolution 3x3x4	4x4x8
4	full connected 5	4x4x12

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Fitting network into embedded

• convert float weights to int8_t
• scale weights into 8bit range
• use double buffer memory trick - to save memory, use only two common buffers for all layers