Use of machine learning for Andruino R2 robot localization based on the wifi signals available in the environment, in a 3 rooms schenario.
Use of machine learning for Andruino R2 robot localization based on the wifi signals available in the environment, in a 3 rooms schenario. Training model available in https://gallery.cortanaintelligence.com/Experiment/Wi-Fi-positioning-system-for-Andruino-R2-ROS-low-cost-robot-Model The robot wanders through a three-room scenario capturing the wifi signals. In the experimentation phase, the room in which it is located is indicated for the creation of the model. After the experimentation phase, the robot is able to determine automáticcally the room in which it is placed just consulting the model created using the actual Wi-Fi signals of the environment. Andruino R2 project have the objective of designing an open educational low-cost (components about 35€/USD) modular and extendable mobile robot based on Android and Arduino, integrated in the cloud, to be used as an educational tool in labs and classrooms of STEM, ICT vocational training or engineering courses, as well as in e-learning or MOOC courses as an alternative or, complementary, to virtual labs and soft robotics simulation. It is a first step introducing what we call “BYOR: Bring Your Own Robot” education policy equivalent to “BYOD: Bring your own devices”. Andruino it is powerful but easy to construct and extremely low cost, as it is based in the student's smartphone. Andruino R2 is compatible with ROS (Robot Operating System) and with iot clouds and try to bring deep learning and other advanced techniques to the STEM's classrooms and VET's Author: @andruinos License: CC-BY-SA https://creativecommons.org/licenses/by-sa/2.0/