200 Pound Behavior Cloning Robot

This is code for the 200 pound behavior cloning robot I am building. It uses two independent drive wheels and a third non-driven swivel wheel. The purpose of this design is simplicity in control, efficient movement on relatively smooth surfaces, and the ability to make very fast turns.

Goals

The goal is to be able to learn how to navigate based on video stimulus, with training accomplished as fast as possible (on the order of minutes). The framework is a fully connected network for learning, with preprocessing to reduce the feature space to a size that is managable with the constraints on training time. The design objective is that all tasks are able to be accomplished in real time on a Rasbperry Pi.

Hardware

There are two incoming data streams: 1) video of the road in front, which is handled on a Raspberry Pi using V4L; and 2) information about control mode, speed and throttle from an Arduino that debounces switches and intercepts standard RC PWM signals. A Dimension Engineering Sabertooth dual 32A motor drive is used to control the two drive motors.

Software

The design of the code is such that the main control structure is clear and simple, with simple constructs for changing hyperparameters. With a few exceptions that are written in C for control and speed, all code is written in OCaml and uses standard libraries.

OCaml (and most garbage collected functional languages) are not performant for tasks involving sequentially accessed numeric computation. Since this is the core problem backpropagation, these operations have been written in C.
A custom frame grabber using V4L was written to meet design specifications. Video pre-processing is based on ad hoc methods to significantly reduce the features space for the fully connected network, as careful attention to the VC dimension is critical to attain the desired performance. A cache- aligned version of the standard Bigarray is introduced to get slightly better performance.

Build Requirements - Main System

You will need the following things on your Raspberry Pi:

System stuff:

• Kernel headers (for v4l2)
• v4l2 and associated utilities (v4l2-dev, v4l2-ctl, etc.)
• libjpeg (basically for debugging camera issues by optionally saving to file)

gcc stuff:

• Modern toolchain (gcc 4.9.2)

OCaml stuff:

• Modern toolchain (4.03.0)

Raspberry Pi specific configuration:

• Add the following to /boot/cmdline.txt
• dwc_otg.fiq_fsm_mask=0x3

LCD display:

• Add the following to /boot/config.txt
• gpu_mem=16
• hdmi_group=2
• hdmi_mode=4

Build Requirements - Arduino

You will need the following things for the Arduino:

• RCArduino (???)

Additional Resources

• RC with Arduino

• http://rcarduino.blogspot.com/2012/11/how-to-read-rc-channels-rcarduinofastlib.html

• http://rcarduino.blogspot.com/2012/01/how-to-read-rc-receiver-with.html

• V4L

• https://linuxtv.org/downloads/v4l-dvb-apis/V4L2-PIX-FMT-YUYV.html

• Install: v4l2, v4l2-utils (v4l2-ctl), xawtv and streamer (debugging)

• V4l Command line

• v4l2-ctl --set-fmt-video=width=160,height=120 --set-ctrl=contrast=255,saturation=0,brightness=200

• streamer -c /dev/video0 -b 16 -o outfile.jpeg

To Do

1. Fix the backpropagation C code
2. Determine proper ad hoc method for image pre-processing
3. Document the Arduino requirements
4. Add the Arduino code base