Just Add Wheels: Leveraging Commodity Laptop Hardware for Robotics and AI Education

Abstract

Along with steady gains in processing power, commodity laptops are increasingly becoming sensor-rich devices. This trend, driven by consumer demand and enabled by improvements in solid-state sensor technology, offers an ideal opportunity to integrate robotics into K–12 and undergraduate education. By adding wheels, motors and a motor control board, a modern laptop can be transformed into a capable robot platform, for relatively little additional cost. We propose designing software and curricula around such platforms, leveraging hardware that many students already have in hand. In this paper, we motivate our laptop-centric approach, and demonstrate a proof-of-concept laptop robot based on an Apple MacBook laptop and an iRobot Create mobile base. The MacBook is equipped with a built-in camera and a three-axis accelerometer unit – we use the camera for monocular simultaneous localization and mapping (SLAM), and the accelerometer for 360 degree collision detection. The paper closes with some suggestions for ways in which to foster more work in this direction. for these activities, and must be reliable enough to allow students and teachers to focus on algorithms and experimentation rather than on hacking the hardware. We believe that it is possible to leverage the ubiquity of laptop computers to fulfill the above, by using a student’s own laptop as part of a capable robot. With the addition of servo motors, a motor control board, and a pair of wheels, a laptop can become a high-performance experiment testbed. We have two immediate goals in this work: first, to show that a useful, laptop-centric robot system can be built, and second to assess the performance of such a hardware/software platform. Our discussion is focused on a software package that we are developing for Apple MacBook line of laptop computers. The MacBook is an Intel-based machine that is able to run Microsoft Windows, Mac OS X, and Linux. As such, it represents a flexible choice for our preliminary demonstration. For ‘wheels’, we use the iRobot