A Low-Cost Lane-Following Algorithm for Cyber-Physical Robots

TL;DR

This paper presents a low-cost, efficient lane-following algorithm for Duckiebots that uses traditional vision and PID control, outperforming most competitors while requiring minimal computational resources.

Contribution

The authors introduce a simple, low-resource autonomous driving algorithm for Duckiebots that is easy to set up and performs competitively in multi-lane navigation.

Findings

Outperformed all but one NeurIPS 2018 AI Driving Olympics finalist.

Uses traditional computer vision and PID control for steering.

Requires minimal computational resources and quick setup.

Abstract

Duckiebots are low-cost mobile robots that are widely used in the fields of research and education. Although there are existing self-driving algorithms for the Duckietown platform, they are either too complex or perform too poorly to navigate a multi-lane track. Moreover, it is essential to give memory and computational resources to a Duckiebot so it can perform additional tasks such as out-of-distribution input detection. In order to satisfy these constraints, we built a low-cost autonomous driving algorithm capable of driving on a two-lane track. The algorithm uses traditional computer vision techniques to identify the central lane on the track and obtain the relevant steering angle. The steering is then controlled by a PID controller that smoothens the movement of the Duckiebot. The performance of the algorithm was compared to that of the NeurIPS 2018 AI Driving Olympics (AIDO) finalists, and it outperformed all but one finalists. The two main contributions of our algorithm are its low computational requirements and very quick set-up, with ongoing efforts to make it more reliable.