Implementing Inertial Measurement Unit on Covid Robot with Complementary and Madgwick Filter

Abstract

Rapidly developing technology causes autonomous robots to develop significantly as well. This causes the demand for autonomous robots to increase in various industrial sectors ranging from agriculture, large-scale manufacturing industries, and even hospitals. Especially in the midst of the Covid-19 pandemic where physical distancing is applied to make autonomous robots that can be used as a substitute for medical personnel. The Inertial Measurement Unit (IMU) is a very important part of an autonomous robot because the IMU can measure 3 axes. The IMU sensor has been integrated with 3 other sensors, namely accelerometer, gyroscope, and magnetometer sensors. However, the data obtained from the sensor has an error value that can cause noise. Therefore, a filter is needed to get high accuracy results. In this study, complementary methods and Madgwick filters were used to reduce noise in the raw data so that the results can be maximized.