A framework for controlling, recording, training, and running a self driving robotic car. Focused now on an implementation that runs on 1/10th scale rc cars with pwm servo steering and ESC throttle controller driven by an Adafruit Servo hat on a RaspberryPi 3.

• Use ZeroMQ and C/Python to create a mesh of components that are flexible and fast
• Run all code on the robot ( pi 3 )
• Manage things with a mobile device via web page and joystick controller

• check docs/bot_setup.md

On your Pi:

• clone the repo
• Check dependencies.txt, build and install dependent libraries.
• add /usr/local/lib to your LD_LIBRARY_PATH env
• copy config_example.json to config.json and modify to match your bot setup
• mkdir build && cd build
• cmake .. && make
• cd ..
• plug a joystick controller into your pi
• python shark.py
• optionally run the webserver. cd web && python webapp.py

Once you are ready, you can install them as services. Check shark.service and sharkweb.service to run on startup.

• Navigate to web page
• Select 'robot'. You should see a live image from camera

• press X on PS3 Sixaxis controls to enable recording
• left analog to steer, right analog forward to throttle
• only recording when throttle is non zero
• press X to disable recording

• Navigate to web page
• Select 'log'
• Select 'view/edit logs'
• observe recorded logs
• if unwanted frames, use slider, 'set trim start', and 'set trim end' to set range
• use 'trim log' button to remove unwanted frames

• copy logs to your PC: scp me@pi.local:~/projects/shark/log/*.jpg ~/projects/shark/log
• train model on your PC: python train.py mymodel
• cp mymodel to pi: scp mymodel me@pi.local:~/projects/shark/model/
• on the pi: python shark.py --model mymodel

• check docs/aws_setup.md
• Navigate to web page
• Select 'ec2' button
• Select 'start ec2'
• Wait for 1 to 2 minutes and select 'check ec2' until the machine is ready.
• Select 'prepare host' to copy code to remote host
• From the home menu select 'log'
• Select 'upload logs'
• From the home menu select 'train'
• Select 'model' and name the new model
• Optionally select 'epochs' and set upper limit of epochs
• Select 'train' to start training. Feedback varies depending on browser. Better luck from a desktop browser. Tested mostly on Firefox.
• When complete, select 'push model' to tell robot predict loop to load that trained result.
• Ready to test self driving
• When you are done with server, select 'release ec2' to shutdown remote machine.

• hit triangle toggle start self driving.
• use dpad up and down to modify throttle scale