Traditional sensor network and robot navigation are based on the map of detecting fields available in advance. The optimal algorithms are explored to solve the energy saving, shortest path problems, etc. However, in practical environment, there are many fields, whose map is difficult to get, and need to detect. This paper explores a kind of ad-hoc navigation algorithm based on the hybrid sensor network without the prior map. The system of navigation is composed of static nodes and mobile nodes. The static nodes monitor events occurring and broadcast. In the system, a kind of cluster broadcast method is adopted to determine the robot localization. The mobile nodes detect the adversary or dangerous fields and broadcast warning message. Robot gets the message and follows ad-hoc routine to arrive the events occurring place. In the whole process, energy saving has taken into account. The algorithms of nodes and robot are given in this paper. The simulate and practical results are available as well.
D. Moore, J. Leonard, D. Rus and S. Teller, “Robust Distributed Networklocalization with Noisy Range Measurements,” Proceedings of the 2nd International Conference on Embedded networked sensor systems, Baltimore, 2004, pp. 50-61.
R. Szewczyk, A. Mainwaring, J. Polastre and D. Culler, “An Analysis of a Large Scale Habitat Monitoring Application,” Proceedings of the 2nd International Conference on Embedded networked sensor systems, Baltimore, 2004, pp. 214-226.
J. Polastre, J. Hill and D. Culler, “Versatile Low Power Media Access for Wireless Sensor Networks,” Proceedings of the 2nd International Conference on Embedded networked sensor systems, Baltimore, 2004, pp. 95-107.
M. Batalin, M. Rahimi, Y. Yu, D. Liu, A. Kansal, G. Suk-hatme, W. Kaiser, M. Hansen, G. Pottie, M. Srivastava and D. Estrin, “Call and Response: Experiments in Sampling the Environment,” Proceedings of ACM Sensor Systems, Los Angeles, 2004.
M. Rahimi, R. Pon, W. J. Kaiser, G. S. Sukhatme, D. Estrin and M. Srivastava, “Adaptive Sampling for Environmental Robotics,” Proceedings of IEEE International Conference on Robotics and Automation, Los Angeles, 2004.
R. Willett, A. Martin and R. Nowak, “Backcasting: Adaptive Sampling for Sensor Networks,” Proceedings of IEEE International Conference on Information processing in sensor networks, New York, 2004, pp. 124-133.
R. Castro, R. Willett and R. Nowak, “Faster Rates in Regression via Active Earning,” Proceedings of Neural Information Processing Systems, Vancouver, 2005. http:// homepages.cae.wisc.edu/rcastro/ECE-05-3. pdf