Design and Implementation of a Compact Integrated Navigation System for Micro Multi-Propeller Multifunction Aerial Robots

Chang Liu Zha; Xi Lun Ding; Yu Shu Yu; Xue Qiang Wang

doi:10.4028/www.scientific.net/AMM.556-562.1553

Paper Titles

Analysis on Energy Loss of SVC Based on TCR and FC Mode Method
p.1537

Research of Single Phase APFC
p.1541

A Novel Multimedia Player Based on Embedded System
p.1545

A High-Speed 64b/66b Decoder Used in SerDes
p.1549

Design and Implementation of a Compact Integrated Navigation System for Micro Multi-Propeller Multifunction Aerial Robots
p.1553

Selecting Optimal Reactive Compensation Device for Power System
p.1560

A 520MHz Wideband Variable Gain Amplifier
p.1564

The Development Research of Solar Energy Power Supply Devices
p.1568

Analysis and Suppression of High-Frequency Harmonics of High Power Photovoltaic Inverter System
p.1573

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 556-562Design and Implementation of a Compact Integrated...

Design and Implementation of a Compact Integrated Navigation System for Micro Multi-Propeller Multifunction Aerial Robots

Abstract:

Micro Multi-propeller Multifunction Aerial Robot (MMAR) needs a low-cost, small-size, low-power and lightweight navigation system. To meet these requirements, a compact navigation system for micro MMARs is designed and implemented. The system consists of a GPS receiver, a MEMS inertial measurement unit (IMU), a barometer, a magnetometer and a dual microprocessors navigation processing platform. According to the characteristics of the sensors used, the optimized Kalman Filter (KF) is designed to implement the navigation data fusion with less computational cost. The test results show that the system’s small size, low power consumption, light weight and better dynamic accuracy can meet the requirements of micro MMAR autonomous flight.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 556-562)

Pages:

1553-1559

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.556-562.1553

Citation:

Cite this paper

Online since:

May 2014

Authors:

Chang Liu Zha*, Xi Lun Ding, Yu Shu Yu, Xue Qiang Wang

Keywords:

Data Fusion, GPS/SINS, Integrated Navigation, MMAR

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] X. Ding, Y. Yu and J. J. Zhu: 2011 IEEE International Conference on Robotics and Automation (2011), p.757.

Google Scholar

[2] X. Ding and Y. Yu: IEEE/ASME Trans. Mechatronics Vol. 18 (2013), p.645.

Google Scholar

[3] Jan Wendel, Eliver Meister, Christian Schlaile, and Gert F. Trommer: Aerospace Science and Technology Vol. 10 (2006), p.527.

Google Scholar

[4] A. Nemra and N. Aouf: IEEE Sensors J. Vol. 10 (2010), p.789.

Google Scholar

[5] D. Li, R. Landry, and P. Lavoie: IEEE/ION Position, Location Navigation Symp. (2008), p.1190.

Google Scholar

[6] M. DeAgostino, A. Manzino, and M. Piras: IEEE/ION Position Locat. Navig. Symp. (2010), p.187.

Google Scholar

[7] M. de Bento, B. Eissfeller and F. Machado: 5th ESA Workshop NAVITEC (2010), p.1.

Google Scholar

[8] A. Vydhyanathan, H. Luinge, M. Uijt de Haag, and M. Braasch: IEEE Conf. Aerospace (2012), p.1.

Google Scholar

[9] A. Fakharian, T. Gustafsson, and M. Mehrfam: 2011 International Conference on Networking, Sensing and Control(ICNSC ) (2011), p.181.

Google Scholar

[10] J. Vasconcelos, C. Silvestre, and P. Oliveira: 47th IEEE Conf. Decision Control (2008), p.1255.

Google Scholar

[11] QIN Yong-Yuan: Inertial navigation (Science Press, Beijing 2006) (in Chinese).

Google Scholar

[12] Fu Meng-Yin, Deng Zhi-Hong, and Yan Li-Ping: Kalman filtering theory with applications in navigation systems (2nded) (Science Press, Beijing 2010) (in Chinese).

Google Scholar