Preliminary laboratory test on navigation accuracy of an autonomous robot for measuring air quality in livestock buildings

Qi, Haixia and Banhazi, Thomas M. and Zhang, Zhigang and Low, Tobias and Brookshaw, Iain J. (2016) Preliminary laboratory test on navigation accuracy of an autonomous robot for measuring air quality in livestock buildings. International Journal of Agricultural and Biological Engineering, 9 (2). pp. 29-39. ISSN 1934-6344

[img]
Preview
Text (Published version)
Preliminary laboratory tes.pdf
Available under License Creative Commons Attribution.

Download (674Kb) | Preview

Abstract

Air quality in many poultry buildings is less than desirable. However, the measurement of concentrations of airborne pollutants in livestock buildings is generally quite difficult. To counter this, the development of an autonomous robot that could collect key environmental data continuously in livestock buildings was initiated. This research presents a specific part of the larger study that focused on the preliminary laboratory test for evaluating the navigation precision of the robot being developed under the different ground surface conditions and different localization algorithm according internal sensors. The construction of the robot was such that each wheel of the robot was driven by an independent DC motor with four odometers fixed on each motor. The inertial measurement unit (IMU) was rigidly fixed on the robot vehicle platform. The research focused on using the internal sensors to calculate the robot position (x, y, θ) through three different methods. The first method relied only on odometer dead reckoning (ODR), the second method was the combination of odometer and gyroscope data dead reckoning (OGDR) and the last method was based on Kalman filter data fusion algorithm (KFDF). A series of tests were completed to generate the robot’s trajectory and analyse the localisation accuracy. These tests were conducted on different types of surfaces and path profiles. The results proved that the ODR calculation of the position of the robot is inaccurate due to the cumulative errors and the large deviation of the heading angle estimate. However, improved use of the gyroscope data of the IMU sensor improved the accuracy of the robot heading angle estimate. The KFDF calculation resulted in a better heading angle estimate than the ODR or OGDR calculations. The ground type was also found to be an influencing factor of localisation errors.


Statistics for USQ ePrint 30712
Statistics for this ePrint Item
Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published version made available under Creative Commons Attribution 3.0 License.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying
Date Deposited: 13 Mar 2017 03:27
Last Modified: 13 Mar 2017 23:53
Uncontrolled Keywords: autonomous robot, air quality, navigation, Kalman filter data fusion, livestock building, robot localization
Fields of Research : 07 Agricultural and Veterinary Sciences > 0702 Animal Production > 070202 Animal Growth and Development
Socio-Economic Objective: D Environment > 96 Environment > 9601 Air Quality > 960103 Farmland, Arable Cropland and Permanent Cropland Air Quality
Identification Number or DOI: 10.3965/j.ijabe.20160902.1189
URI: http://eprints.usq.edu.au/id/eprint/30712

Actions (login required)

View Item Archive Repository Staff Only