Cooperative Search and Rescue with Artificial Fishes Based on Fish-Swarm Algorithm for Underwater Wireless Sensor Networks

This paper presents a searching control approach for cooperating mobile sensor networks. We use a density function to represent the frequency of distress signals issued by victims. The mobile nodes' moving in mission space is similar to the behaviors of fish-swarm in water. So, we take the mobile node as artificial fish node and define its operations by a probabilistic model over a limited range. A fish-swarm based algorithm is designed requiring local information at each fish node and maximizing the joint detection probabilities of distress signals. Optimization of formation is also considered for the searching control approach and is optimized by fish-swarm algorithm. Simulation results include two schemes: preset route and random walks, and it is showed that the control scheme has adaptive and effective properties.

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

[2]  Ryan Moody,et al.  MASA: a multi-AUV underwater search and data acquisition system , 2002, OCEANS '02 MTS/IEEE.

[3]  Krishnendu Chakrabarty,et al.  Sensor deployment and target localization based on virtual forces , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[4]  Christos G. Cassandras,et al.  A minimum-power wireless sensor network self-deployment scheme , 2005, IEEE Wireless Communications and Networking Conference, 2005.

[5]  Anne-Marie Kermarrec,et al.  Connectivity-Guaranteed and Obstacle-Adaptive Deployment Schemes for Mobile Sensor Networks , 2009, IEEE Trans. Mob. Comput..

[6]  Ivan Stojmenovic,et al.  Biconnecting a Network of Mobile Robots Using Virtual Angular Forces , 2010, 2010 IEEE 72nd Vehicular Technology Conference - Fall.

[7]  Krishna R. Pattipati,et al.  Distributed Algorithms for Energy-Efficient Even Self-Deployment in Mobile Sensor Networks , 2014, IEEE Transactions on Mobile Computing.

[8]  Junzhi Yu,et al.  A simplified propulsive model of bio-mimetic robot fish and its realization , 2005, Robotica.

[9]  George J. Pappas,et al.  Self-triggered coordination of robotic networks for optimal deployment , 2011, Proceedings of the 2011 American Control Conference.

[10]  Anne-Marie Kermarrec,et al.  Connectivity-Guaranteed and Obstacle-Adaptive Deployment Schemes for Mobile Sensor Networks , 2008, IEEE Transactions on Mobile Computing.

[11]  Yoseph Bar-Cohen,et al.  Electroactive Polymer (EAP) Actuators as Artificial Muscles: Reality, Potential, and Challenges, Second Edition , 2004 .

[12]  H. Bruyninckx,et al.  Active Sensing for Robotics – A Survey , 2002 .

[13]  Gaurav S. Sukhatme,et al.  Mobile Sensor Network Deployment using Potential Fields : A Distributed , Scalable Solution to the Area Coverage Problem , 2002 .

[14]  Paolo Fiorini,et al.  Search and Rescue Robotics , 2008, Springer Handbook of Robotics.

[15]  Jorge Cortés,et al.  Coverage Optimization and Spatial Load Balancing by Robotic Sensor Networks , 2010, IEEE Transactions on Automatic Control.

[17]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[18]  Arthur C. Sanderson,et al.  Robotic deployment of sensor networks using potential fields , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[19]  J. Crowell,et al.  Workhorse AUV – A cost-sensible new Autonomous Underwater Vehicle for Surveys/Soundings, Search & Rescue, and Research , 2005, Proceedings of OCEANS 2005 MTS/IEEE.

[20]  Miodrag Potkonjak,et al.  Coverage problems in wireless ad-hoc sensor networks , 2001, Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No.01CH37213).

[21]  Y. A. Khalaf,et al.  High linearity CMOS variable gain amplifier for UWB applications , 2012 .

[22]  Hsing-Chih Tsai,et al.  Modification of the fish swarm algorithm with particle swarm optimization formulation and communication behavior , 2011, Appl. Soft Comput..

[23]  Calin Belta,et al.  Receding horizon surveillance with temporal logic specifications , 2010, 49th IEEE Conference on Decision and Control (CDC).

[24]  Jian Li,et al.  A micro biomimetic manta ray robot fish actuated by SMA , 2009, 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[25]  Christos G. Cassandras,et al.  Cooperative receding horizon control for multi-agent rendezvous problems in uncertain environments , 2010, 49th IEEE Conference on Decision and Control (CDC).

[26]  Y. Cohen Electroactive Polymer (EAP) Actuators as Artificial Muscles - Reality , 2001 .

[27]  Christos G. Cassandras,et al.  Distributed Coverage Control and Data Collection With Mobile Sensor Networks , 2010, IEEE Transactions on Automatic Control.