MATEC Web Conf.
Volume 220, 20182018 The 2nd International Conference on Mechanical, System and Control Engineering (ICMSC 2018)
|Number of page(s)||5|
|Section||Intelligent Robot and Control Technology|
|Published online||29 October 2018|
Noise Reduction on the Tilt Sensor for the Humanoid Robot Balancing System Using Complementary Filter
Computer Engineering, Faculty of Computer Science, Brawijaya University, Malang, Indonesia
The aim of the research is to present the design of a system which is capable for noise reduction on the reading of the tilt sensor data used in a balancing system on humanoid robots. Humanoid robot is a robot that has body construction like a human. Humanoid robots can make a movement by walking with both legs. In order to walk properly, humanoid robot needs to have a proper balancing system. The humanoid robot balancing system consists of tilt sensors, either an accelerometer or a gyroscope sensor, main processing unit and actuator in the form of servo motors on each robot’s joint. The tilt sensor is used to detect tilt angle of the robot body. When sensor gives information about the robot is in a position of tilt angle that will cause the robot to fall, the main processor will send commands to the actuators to do the balancing action so the robot will go to a balancing position. Unfortunately, the tilt angle reading value obtained from tilt sensor, still contain noise. That noise makes the reading of the tilt angle of the robot body to be inaccurate. Therefore, a filtering method is required to reduce the noise. In this research, complementary filter is used to overcome this problem. From the test result, the complementary filter is able to reduce noise on the tilt sensor data value reading. The optimum filtering result was obtained with the use of filter coefficient of 0.96 and sampling time of 50ms, where the average tilt angle error value is 0.775° on the x-axis and 0.691° on the y-axis. In another test, the robot is able to perform a balancing action based on the output of the complementary filter. So the robot was always able to maintain its position on balanced condition.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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