This article discusses how machine learning techniques are being applied to a small, lightweight robot designed by Nikodem Bartnik. The robot, based on a two-wheeled design with tank-style steering, is capable of autonomously navigating a simple racetrack delimited by cardboard barriers.
To achieve autonomous navigation, the robot is equipped with an Arduino Uno microcontroller, a Slamtec RPLIDAR sensor for mapping its surroundings, a Bluetooth link for communication, and an SD card for data storage. Initially, the robot was driven manually around the racetrack multiple times while collecting LIDAR data.
Using this collected data, along with control inputs, a data set was created to train a machine learning model. Feature selection techniques were then employed to refine and identify the most relevant data points for completing the driving task. Nikodem explains the process of model creation and refinement, enabling the robot to drive itself in various racetrack designs.
The use of lightweight hardware, such as an Arduino Uno, demonstrates that machine learning techniques can be implemented on simpler and more compact platforms. This offers a potential alternative to the traditional image of machine learning being associated with large-scale datacenters and high-performance GPUs.
Overall, this robot serves as a practical example of applying machine learning techniques to small embedded systems. It showcases the possibilities of integrating intelligence and autonomy into lightweight robotic platforms.
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