A Sensing and Robot Navigation of Hybrid Sensor Network

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.

[1]  David E. Culler,et al.  Versatile low power media access for wireless sensor networks , 2004, SenSys '04.

[2]  A. Korostelev On minimax rates of convergence in image models under sequential design , 1999 .

[3]  Sonia Martínez,et al.  Coverage control for mobile sensing networks , 2002, IEEE Transactions on Robotics and Automation.

[4]  David C. Moore,et al.  Robust distributed network localization with noisy range measurements , 2004, SenSys '04.

[5]  Michael Horstein,et al.  Sequential transmission using noiseless feedback , 1963, IEEE Trans. Inf. Theory.

[6]  Gaurav S. Sukhatme,et al.  Call and response: experiments in sampling the environment , 2004, SenSys '04.

[7]  Gaurav S. Sukhatme,et al.  Adaptive sampling for environmental robotics , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[8]  Oussama Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Autonomous Robot Vehicles.

[9]  John Anderson,et al.  An analysis of a large scale habitat monitoring application , 2004, SenSys '04.

[10]  R. Nowak,et al.  Backcasting: adaptive sampling for sensor networks , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[11]  Robert D. Nowak,et al.  Faster Rates in Regression via Active Learning , 2005, NIPS.