By Topic

A new MEMS Gyro north finding approach using LSM for mobile robot heading detection

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Yuanlong Wei ; Department of Mechanical Engineering, Pusan National University, Busan, South Korea ; Mincheol Lee

In this paper, we present a new approach for mobile robot heading detection using MEMS Gyro north finding method. Based on this, the robot heading angle measurement scheme is proposed; improved north finding theory and algorithm are also explained. Several approaches are applied to confirm system's precision and effectiveness. In order to find out the heading angle, we use a single axis MEMS gyroscope and accelerometer package to sense the angle between the robot heading direction and the north. At the same time, we apply the accelerometer to sense the effect of the earth gravity to ensure the targeting function on both horizontal road and rugged road. To reach enough estimation accuracy and reduce detection time, we apply Least Square Method (LSM) and Extended Kalman Filter (EKF) for the signal fitting, filtering and data fusion. Through a turn-table, we setup a carouseling system to decrease the substantial bias effect on gyroscope's heading angle. For the evaluation of the proposed method, this system is implemented to the Pioneer robot platform. The performance and heading error are analyzed after the test. From the simulation and experimental results, system's accuracy, usefulness and adaptability are shown.

Published in:

SICE Annual Conference (SICE), 2012 Proceedings of

Date of Conference:

20-23 Aug. 2012