Shun-cai YAO(姚舜才)1, Jin-dong TAN(谭劲东)2, Hong-xia PAN(潘宏侠)3
1. School of Information and Communication Engineering, North Uni versity of China, Taiyuan 030051, China;2. Dept. of Electrical Computer Engineering, Michigan Technological Universi ty, Houghton 49931-1295, USA;3. School of Mechanical Engineering Automation, North University of China, T aiyuan 030051, China
Abstract-Traditional sensor network and robot navigation are b ased on the map of detecting the fields available in advance. The optimal algori thms are developed to solve the energy saving, the shortest path problems, etc. However, in the practical environment, there are many fields, whose map is diffi cult to get, and needs to be detected. In this paper a kind of ad-hoc navigatio n algorithm is explored, which is based on the hybrid sensor network without the prior map in advance. The navigation system is composed of static nodes and dyn amic nodes. The static nodes monitor the occurrances of the events and broadcast them. In the system, a kind of algorithm is to locate the robot, which is based on cluster broadcasting. The dynamic nodes detect the adversary or dangerous fi elds and broadcast warning messages. The robot gets the message and follows ad- hoc routine to arrive where the events occur. In the whole process, energy savin g has been taken into account. The algorithms, which are based on the hybrid sen sor network, are given in this paper. The simulation and practical results are a lso available.
Key words-Hybrid sensor network; robot navigation; rout ine planning; energy saving algorithm
Manuscript Number: 1674-8042(2010)01-0074-07
dio: 10.3969/j.issn.1674-8042.2010.01.016
References
[1]D. Moore, J. Leonard, D. Rus, et al, 2004. Robust Distributed Networ klocalization with Noisy Range Measurements. Proc. of SenSys.
[2]R. Szewczyk, A. Mainwaring, et al, 2006. An Analysis of a Large Scal e Habitat Monitoring Application. Proc. of SenSys.
[3]J. Polastre, J. Hill, D. Culler, 2007. Versatile Low Power Media Acc ess for Wireless Sensor Networks. Proc. of SenSys.
[4]M. Batalin, M. Rahimi, Y. Yu, 2006. Call and Response: Experiments i n Sampling the Environment. Proceedings of ACM SenSys.
[5]M. Rahimi, R. Pon, W. J. Kaiser, 2006. Adaptive Sampling for Environ mental Robotics. Proceedings of IEEE Int. Conf. on Robotics and Automation, ICRA , New Orleans, LA.
[6]R. Willett, A. Martin, R. Nowak, 2006. Backcasting: Adaptive Samplin g for Sensor Networks. Proceedings of IPSN.
[7]R. Castro, R. Willett, R. Nowak, 2007. Faster Rates in Regression Vi a Active Learning. Proceedings of NIPS.
[8]M. Horstein, 1963. Sequential decoding using noiseless feedback. IEEE Trans. Information Theory, 9(3): 136-143.
[9]J. Borenstein, H. R. Everett, 2006. Navigating Mobile Robots: Sensor s and Techniques, John Wiley & Sons.
[10]M. Burnashev, K. S. Zigangirov, 2004. An interval estimation proble m for controlled observations. Problems in Information Transmission, 10: 223-231.
[11]O. Khatib, 2006. Real-time Obstacle Avoidance for Manipulators and Mobile Robots. The International Journal of Robotics Research, 5(1): 90-98.
[12]J. Cortes, S. Martinez, T. Karatas, et al, 2006. Coverage Control f or Mobile Sensing Networks. IEEE Transactions on Robotics and Automation, New Or leans, USA, p. 243-255.
[13]A. Korostelev, 2007. On minimax rates of convergence in image model s under sequential design. Statistics and Probability Letters , 43: 369-375.
[14]G. Golubev, B. Levit, 2005. Sequential recovery of analytic periodi c edged in the binary image models. Mathematical Methods of Statistics , 12: 95-115.
[Full Text View]
