Page 200 - Autonomous Mobile Robots
P. 200
184 Autonomous Mobile Robots
be used to improve the quality of the global map. Improvements in colocation
accuracy should be possible allowing either the extension of the range over
which cooperative localization is possible or reducing the separation of the
targets so that they may be mounted on one robot thus allowing cooperative
mapping with only two robots. These improvements in colocation accuracy
would primarily come from over-sampling the least squares fitting algorithm.
Although multiple sensors and multiple landmarks have been adopted in
the proposed navigation system, they have been independently investigated
and tested so far. A natural extension of future research is to investigate the
integration of three landmark-based navigation algorithms. Moreover, the pro-
posed navigation algorithms have potential applications for service robots in
homes, offices, and hospitals. It can also be used for outdoor beacon-based
navigation such as GPS navigation systems.
REFERENCES
1. Astrom K. (1992). A correspondence problem in laser guided navigation, in
Proceedings of Symposium on Image Analysis, D. Eriksson and E. Benojsson
(eds), Sweden, pp. 141–144.
2. Chee B.-Y. and Lang S. Y. T. (1996). A random sampling approach to land-
mark detection for mobile robot localization, Proceedings of International
Conference on Mechatronics, 1, 41–44.
3. Hu H. and Gu D. (2000). Landmark-based navigation of industrial mobile robot,
International Journal of Industrial Robot, 27, 458–467.
4. Everett H. R. (1995). Sensors for Mobile Robots — Theory and Application.
A K Peters Ltd., Natick, MA.
5. BarshanB.andDurrant-WhyteH.F.(1995). Inertialsensingformobilerobotics,
IEEE Transactions on Robotics and Automation, 11, 328–342.
6. Kleeman L. (1992). Optimal estimation of position and heading for mobile
robots using ultrasonic beacons and dead reckoning, in Proceedings of
IEEE International Conference on Robotics and Automation, Nice, France,
pp. 2582–2587.
7. Cooper S. and Durrant-Whyte H. F. (1994). A Kalman filter model for GPS
navigation of land vehicles, in Proceedings of IEEE/RSJ/GI International
Conference on Intelligent Robotic Systems, Munich, Germany, pp. 157–163.
8. Borenstein J., Everett H. R., Feng L., and Wehe D. (1997). Mobile robot posi-
tioning — sensors and technologies, Journal of Robotic Systems, Special Issue
on Mobile Robots, 14, 231–249.
9. Sugihara K. (1988). Some location problems for robot navigation using a single
camera, Journal of Computer Vision, Graphics and Image Processing, 42,
112–129.
10. Avis D. and Imai H. (1990). Locating a robot with angle measurements, Journal
of Symbolic Computation, 10, 311–326.
11. KrotovE.(1989). Mobilerobotlocalisationusingasingleimage, in Proceedings
of IEEE International Conference on Robotics and Automation, Scottsdale, AZ,
Vol. 2, pp. 978–983.
© 2006 by Taylor & Francis Group, LLC
FRANKL: “dk6033_c004” — 2006/3/31 — 16:42 — page 184 — #36