behaviorsuite's Introduction

Behavior Suite

This project presents different approaches to the follow-the-line exercise but using artificial intelligence to complete the circuits. The solutions presented are:

Using classification networks.
Using regression networks.
Using reinforcement learning.
Solution for real robots.

Index of the project:

Design

Behavior Suite project has the following structure (WIP):

Current Status

We are currently redesigning the project. The following functional requirements have been specified:

Number	Description	Status
RF01	Changing run-time intelligence	WIP
RF02	Save tagged dataset (IMG + ROSbags, cmd-vel)	WIP
RF03	'Manual' Autopilot. User solution (OpenCV)	DONE
RF04	Teleoperation	WIP
RF05	Benchmarking (neuronal network vs groundthruth, checkpoints, center desviation, ...)	-
RF06	Support for different environments (TensorFlow, Keras, Pytorch, OpenCV, ...)	WIP
RF07	User profiles (configuration file)	-

The following table contains non-functional requirements:

Number	Description	Status
RN01	Real time	-
RN02	Memory	-
RN03	GPU-Ready	-

References

Zhicheng Li, Zhihao Gu, Xuan Di, Rongye Shi. An LSTM-Based Autonomous Driving Model Using Waymo Open Dataset. arXiv e-prints, art.arXiv:2002.05878, Feb 2020. https://arxiv.org/abs/2002.05878

Pei Sun et at. Scalability in Perception for Autonomous Driving: Waymo Open Dataset. arXiv e-prints, art.arXiv:1912.04838, Dec 2019. https://arxiv.org/abs/1912.04838 (Waymo Open Dataset)[https://waymo.com/open/]

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