全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

Wireless Sensor Network and RFID Fusion Approach for Mobile Robot Navigation

DOI: 10.1155/2013/157409

Full-Text   Cite this paper   Add to My Lib

Abstract:

There are numerous applications for mobile robots that require relatively high levels of speed and precision. For many systems, these two properties are a tradeoff, as oftentimes increasing the movement speed can mean failing to detect some sensors. This research attempts to create a useful and practical system by combining a wireless sensor network with a passive radio frequency identification system. The sensor network provides fast general navigation in open areas and the radio frequency identification system provides precision navigation near static obstacles. By fusing the data from both systems, we are able to provide fast and accurate navigation for a mobile robot. Additionally, with WSN nodes and passive RFID tag mats, the system infrastructure can be easily installed in existing environments. 1. Introduction In ubiquitous robotics, there are several applications for mobile robots that require relatively high levels of precision. In this research, we developed a system that balances the trade-off of speed and precision for mobile robot navigation around static obstacles. The system uses higher speeds when the area is free of obstacles, via a wireless-sensor-network-based (WSN) navigation approach. Around obstacles, or when a specific path or pose is required, the system incorporates a Radio Frequency IDentification (RFID) system, for precise navigation. By fusing the data from both systems using a simple, vector-based approach, we are able to provide fast and accurate navigation for a mobile robot. RFID tag mats were also developed which, along with easily installed wireless sensor nodes, make for quickly deployed system infrastructure. This system focuses on using this fusion approach for indoor mobile robot navigation with static obstacle avoidance. While the avoidance of dynamic obstacles is also key for ubiquitous robotics, there is still room for improvement in static obstacle avoidance. Moreover, the vast majority of applications for indoor robot navigation have a large static obstacle component to them. It is for this reason that we chose to focus first on this aspect. A simple taxonomy for robot sensors (pertaining to navigation) could be listed as such: vision (cameras, etc.), range-finding (laser, sonar, etc.), inertial (encoders, etc.), active beacons (active RFID, WSN, etc.), and passive beacons (passive RFID, magnetic strips, etc.). All of these systems have their advantages and disadvantages. For instance, range-finding can suffer from inaccuracy when there are few reflective surfaces and vision systems often have a high

References

[1]  G. Enriquez and S. Hashimoto, “Wireless sensor network-based navigation for human-aware guidance robot,” in Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO '08), pp. 2034–2039, Bangkok, Thailand, February 2009.
[2]  S. Park and S. Hashimoto, “Autonomous mobile robot navigation using passive RFID in indoor environment,” IEEE Transactions on Industrial Electronics, vol. 56, no. 7, pp. 2366–2373, 2009.
[3]  G. Enriquez, S. Park, and S. Hashimoto, “Wireless sensor network and RFID sensor fusion for mobile robots navigation,” in Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO '10), pp. 1752–1756, Tianjin, China, December 2010.
[4]  Peter Harrop, “Active RFID, 2006–2016,” IDTechEx, July 2006.
[5]  S. Sarma and D. W. Engels, “On the future of RFID tags and protocols,” Auto-ID center white paper, June 2003.
[6]  P. Y. Chen, W. T. Chen, Y. C. Tseng, and C. F. Huang, “Providing group tour guide by RFIDs and wireless sensor networks,” IEEE Transactions on Wireless Communications, vol. 8, no. 6, pp. 3059–3067, 2009.
[7]  J. Collins, “BP tests RFID sensor network at U.K. plant,” June 2006, http://www.rfidjournal.com/article/articleview/2443/1/1,.
[8]  M. L. McKelvin, M. L. Williams, and N. M. Berry, “Integrated radio frequency identification and wireless sensor network architecture for automated inventory management and tracking applications,” in Proceedings of the Richard Tapia Celebration of Diversity in Computing Conference (TAPIA '05), pp. 44–47, October 2005.
[9]  J. Sung, T. S. Lopez, and D. Kim, “The EPC Sensor Network for RFID and WSN integration infrastructure,” in Proceedings of the 5th Annual IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops '07), pp. 618–621, White Plains, NY, USA, March 2007.
[10]  W. Wang, J. Sung, and D. Kim, “Complex event processing in EPC sensor network middleware for both RFID and WSN,” in Proceedings of the 11th IEEE Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing (ISORC '08), pp. 165–169, Orlando, Fla, USA, May 2008.
[11]  D. Seo, D. Won, G. Yang, M. Choi, S. Kwon, and J. Park, “A probabilistic approach for mobile robot localization under RFID Tag infrastructures,” in Proceedings of the International Conference on Control, Automation, and Systems (ICCAS '05), pp. 1797–1801, 2005.
[12]  S. Han, H. Lim, and J. Lee, “An efficient localization scheme for a differential-driving mobile robot based on RFID system,” IEEE Transactions on Industrial Electronics, vol. 54, no. 6, pp. 3362–3369, 2007.
[13]  S. Park and S. Hashimoto, “An approach for mobile robot navigation under randomly distributed passive RFID environment,” in Proceedings of the IEEE International Conference on Mechatronics (ICM '09), pp. 1–6, Malaga, Spain, April 2009.
[14]  G. Enriquez, S. Park, and S. Hashimoto, “Wireless sensor network-based mobile robot navigation with RFID path refinement,” in Proceedings of the 27th Annual Conference of the Robotics Society of Japan, 2009.
[15]  S. E. Yu and D. Kim, “Distance estimation method with snapshot landmark images in the robotic homing navigation,” in Proceedings of the 23rd IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '10), pp. 275–280, Taipei, Taiwan, October 2010.
[16]  W. Huang, A. Osothsilp, and F. Pourboghrat, “Vision-based path planning with obstacle avoidance for mobile robots using linear matrix inequalities,” in Proceedings of the 11th International Conference on Control, Automation, Robotics and Vision (ICARCV '10), pp. 1446–1451, Singapore, December 2010.
[17]  J. B. Kim and B. K. Kim, “Calibrated localization with 2-D laser range finder for indoor mobile robots,” in Proceedings of the International Conference on Control, Automation and Systems (ICCAS '10), pp. 551–556, Gyeonggi-do, Republic of Korea, October 2010.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133