Self Driving RC Car Code

Related tags

Deep Learning derplearning
Overview

Derp Learning

Derp Learning is a Python package that collects data, trains models, and then controls an RC car for track racing.

Hardware

You will need access to a drill and a soldering kit.

Vehicles

The following vehicles have been tested and are supported:

SparkFun Jetbot

If you prefer something smaller, slower, with zero turn radius powered by individual-wheel drive motors the Sparkfun JetBot Kit $275 includes all parts, except for the optional IMU and Dualshock 4 controller. Please follow the setup guide at the site.

  • Adafruit BNO055 9-DOF Absolute Orientation IMU $35
  • Playstation Dualshock 4 Controller $45

Traxxas-based Vehicles

Each of the following vehicles has been tested and works with the platform. 3D printed parts are TBD.

  • LaTrax Rally (1/18 scale brushed) $120

  • Traxxas Slash (1/10 scale brushed) $240

  • Traxxas E-Revo (1/16 scale brushless) $250

  • Jetson Nano $100

  • Pololu Micro Maestro 6-Channel USB Servo Controller $19

  • Dual Lock Reclosable Fastener $19

  • USB Battery $16

  • USB to Barrel Cable $7

  • USB Jumper $5

  • Brass Spacers/Offsets M2+M3 Offsets $17

  • Nylon Washers M2+M3 $8

  • Breadboard Jumper Wires $7

  • Wifi Card $20

  • Wifi Antenna $3

  • Camera $30

  • 64GB MicroSD Card $12

  • Adafruit BNO055 9-DOF Absolute Orientation IMU $35

  • Playstation Dualshock 4 Controller $45

Software

This software has been tested on Ubuntu 18.04 using Python 3.6 on both x86_64 and aarch64 architectures.

bash install.sh

Collect Data

bin/drive.py config/rally.yaml

Label Data

bin/label.py data/recording-YYYYMMDD-HHMMSS-HOSTNAME

Build a dataset and train cloning model

bin/clone.py config/brain-clone.yaml

Test model

bin/drive.py config/rally.yaml
Comments
  • Camera Calibration

    Camera Calibration

    Hi,

    I woulld like to start with camera calibration but you are looking for circles

        ret, corners = cv2.findCirclesGrid(
        gray_frame, pattern_shape, flags=cv2.CALIB_CB_ASYMMETRIC_GRID
    )

    which fails for me, According to the docs it should be cv2.findChessboardCorners no ?

    opened by vkuehn 2
  • Clarify messages

    Clarify messages

    • Control -> Action and State -> Controller
    • timestamps now in uint64 nanoseconds
    • revamped colors in labeler a bit with message changes from Label -> Quality
    • Added index to Camera/Imu and removed some fields that are in config or too sensor-specific
    • Modularized joystick, imu, and camera to make run simpler and use the return to exit the program upon sensor fail
    • Fixed some known issues with install (evdev and pip packages needing to be installed sequentially)
    opened by notkarol 1
  • Install Script mounts non-existent memory device on non-jetson computers

    Install Script mounts non-existent memory device on non-jetson computers

    install/install.sh currently mounts a spare memory device on the partition it's run on regardless of whether the device has that memory device. This prevents such devices from rebooting sucessfully.

    bug 
    opened by John-Ellis 1
  • IMU calibration loading

    IMU calibration loading

    Autoloading IMU calibration data does not improve the IMU calibration status. Calibration saving appears to work fine so the problem is likely in loading the saved calibrations when initializing the IMU.

    bug 
    opened by John-Ellis 1
  • Master Branch is currently Broken

    Master Branch is currently Broken

    Dev Branch is also currently broken at the moment. We need to repair Dev, validate, and merge to master so that outside users will have access to reliable working code.

    opened by John-Ellis 0
  • key programs have poor UI documentation

    key programs have poor UI documentation

    Currently several important programs lack sufficient documentation of their calling arguments.

    Label.py also would benefit from a more clear description of the keyboard interface

    enhancement 
    opened by John-Ellis 0
  • Logging to identify issues with chronic controller lag

    Logging to identify issues with chronic controller lag

    Solution to issue #1 did not fix all our problems. One of the cars often has large input lag and perhaps affects ability to train. We need better logging to dive into root cause.

    1. Uniquely identify each message by a hash or index so we can track it through the process.
    2. Log exact time every message is received in daemon_ds4.
    3. Log exact time every message is sent by daemon_ds4.
    4. Log exact time every message is received in dualshock4.py.
    5. Log how long we spend in each part of sense, plan, act (while autonomous, not-recording, and, and recording)

    We want to keep #4 below 15ms if possible. We want to identify any spikes in how long messages are buffered at any step. We want to identify fraction messages that are dropped.

    bug 
    opened by notkarol 0
Owner
Not Karol
Automation & Self-Organization
Not Karol
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