The Mule: the forbidden love-child issuing from a male donkey (a jack) and a mare. Sure footed. Even tempered. Ok, maybe your friends laugh at you from their fancy horses, their thoroughbreds, their imported Arabians; but we'll see who gets the last laugh!

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muleAI is inspired by the DonkeyCar project. We decided not to fork, but to rewrite.

muleAI is a systematic re-implementation of some core functionality with some priorities in mind:

• Simplicity and consistency in modular design
• Clean, well-structured implementation conforming to standard software design principles

muleAI is a lightweight python library that facilitates research and development in autonomous mobility at RC-scale.

muleAI is a foundation for further experiments in mobility, autonomous hardware, embedded AI, Internet Of Things, ...

Release candidate 1.0

Dependencies

UPDATE? Tensor flow 1.8 (includes keras as tf.keras)

Features - Mule autonomous vehicle operations software platform

1. Extended configuration file, YAML format
   • As much as possible is exposed to configuration, allowing rapid changing of parameters during racing days
2. Command line interface exposed using click
3. States are saved using a timestamp, the time.time() * 1000 (Unix standard, number of milliseconds since 1970)
   • Allows for fast timestep analysis, strict ordering and alignment of states
4. Modular part classes inherit from Abstract Base Class
   • Enforce proper interface for all new parts
   • Include default behaviors such as class strings
5. Extensive logging messages throughout the project, for faster debugging
6. New adjustment method for PS3 controller, DPad selects a value, and up/down to change the value allowing performance changes as the car is driving
   • D-pad left/right on the PS3 controller iterates over adjustment settings
   • D-pad up/down on the PS3 controller adjusts that value by SHIFT amount
   • Currently able to adjust max forward/reverse throttle and steering
7. Images are saved directly to numpy arrays, timestamped, and zipped for fast transfer to training

Behaviour changes

1. No support for any installation or setup method - project is run directly from the git directory
2. Linux and Mac OS are tested, not Windows
3. Training of models is in a separate module