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A fractal conductivity-based approach to mobile sensor networks in a potential field

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Abstract

This paper presents the design of multi-robots or multiple mobile sensors and gives the results of the team behavior of them in a potential field in a collaborative manner. The aim is to prove the concept of the fractal conductivity-based approach to mobile sensor networks in a potential field. We attempted to create multi-robots having similar inherent nature, similar background knowledge, and the same mission as relevant bodies, the creation of some sort of attraction force between them is achieved with the application of fractal conductivity of belief functions. We attempted to develop multi-robots capable of carrying out an orchestrated movement in an environment having some kind of potential field such as a chemical substance contaminated into sea or lake water. The basis of the theory of behavior in a potential field is established. The fractal conductivity approach is explained. A mobile sensor structure having an input, output, and internal belief functions is designed. Internal structures of mobile sensors are implemented by software. A mobile sensor network composed of mobile sensor nodes is developed. Two communities of interest groups have been constituted. Collaborative behaviors of multi-robots in a potential field are investigated. The mobile sensors move towards a target by sensing the potential field at their locations, broadcasting the information composed of locally sensed intensity of the potential field around them and the infrastructure knowledge of themselves to other sensors and receiving the information of the potential field intensities together with the identity information that is being broadcasted from the other mobile sensors inside the potential field. The target is a source creating the potential field. A mobile sensor is a body incorporating a prior knowledge base that is the identity information in the form of a fractal belief function inside itself. After developing the theoretical basis for sensing potential field and movement, a fractal conductivity approach, which is based on fractal natures of the belief functions, is applied to this basic approach to obtain the movements of the multi-robots as a coordinated team, i.e., grouped as relevant bodies. The operation of mobile sensors based on time division multiple access method is achieved. The results of coordinated movement and obstacle avoidance have been demonstrated by the simulation results.

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References

  1. Arai T, Pagello E, Parker LE (2002) Guest editorial: advances in multi-robot systems. IEEE Trans Robot Autom 18(5):655–661

    Article  Google Scholar 

  2. Arkin R (1998) Reactive robotic systems. The handbook of brain theory and neural networks. MIT Press, Cambridge, pp 793–796

  3. Arkin RC, Balch T (1998) Cooperative multiagent robotic systems. In: Kortenkamp D, Bonasso RP, Murphy R (eds) Artificial intelligence and mobile robots. MIT Press, Cambridge

  4. Cao Y, Fukunaga AS, Kahng AB (1997) Cooperative mobile robotics: antecedents and directions. Auton Robots 4(1):7–27

    Article  Google Scholar 

  5. Chong CY, Kumar SP (2003) Sensor networks; evolution, opportunities and challenges. In: Proc IEEE 91(8):1247–1256

  6. Erkmen A (1989) Information fractals for approximate reasoning in sensor-based robot grasp control. PhD Thesis, George Mason University

  7. Erkmen A, Stephanou HE (1990) Information fractals for an evidential classifier. IEEE Trans Syst Man Cybern 20(5):1103–1114

    Article  Google Scholar 

  8. Gat E (1998) On three-layer architectures. Artificial Intelligence and Mobile Robotics, AAAI Press, pp 195–210

  9. Howard A, Mataric MJ, Sukhatme GS (2002) Mobile sensor network deployment using potential fields: a distributed, scalable solution to the area coverage problem. In: Proc 6th Int Symp on Distributed Autonomous Robotics Systems, Japan

  10. Howard A, Mataric MJ, Sukhatme GS (2002) An incremental self-deployment algorithm for mobile sensor networks. Auton Robots Spec Iss on Intelligent Embedded Systems 13(2):113–126

    MATH  Google Scholar 

  11. Krogh BH (1984) A generalized potential field approach to obstacle avoidance control, robotics research: the next five years and beyond. Soc Manuf Eng

  12. Kumar V, Rus D, Singh S (2004) Robot and sensor networks for first responders. In: Pervasive Computing, IEEE CS and ComSoc, October-December

  13. Mataric MJ, Howard A, Sukhatme GS (2002) Mobile sensor network deployment using potential fields: a distributed, scalable solution to the area coverage problem. In: Proc 6th Int Symp on Distributed Autonomous Robotic Systems

  14. Muhammed RP, Erkmen AM, Erkmen I (2006) Scalable self-deployment of mobile sensor networks: a fluid dynamics approach. IEEE/RSJ (Robotics Society of Japan) International Conference on Intelligent Robots and Systems IROS (in press)

  15. Nicolescu MN, Mataric MJ (2002) A hierarchical architecture for behavior-based robots. In: Proc First Int Joint Conf on Autonomous Agents and Multi-Agent Systems, Bologna, Italy, July 15–19

  16. Parker LE (1998) ALLIANCE: An architecture for fault tolerant multi-robot cooperation. IEEE Trans Robot Autom 14(2):220–240

    Google Scholar 

  17. Poduri S, Sukhatme GS (2004) Constrained coverage for mobile sensor networks. In: Proc IEEE Int Conf on Robotics and Automation, New Orleans, LA

  18. Popa DO, Helm C (2004) Robotic deployment of sensor networks using potential fields. In: Proc IEEE Int Conf on Robotics and Automation, New Orleans, LA

  19. Popa DO, Wen J (1996) Nonholonomic path-planning with obstacle avoidance. In: Proc Int Conf on Robotics and Automation, Minneapolis

  20. Shucker B, Bennett JK (2004) Scalable control of distributed robotic macrosensors. In: 7th International Symposium on Distributed Autonomous Robotic Systems

  21. Zou Y, Chakrabarty K (2003) Sensor deployment and target localization based on virtual forces. In: Proc IEEE Infocom Conference, vol. 2

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Authors and Affiliations

  1. Communications Electronics and Information Systems Division, Turkish General Staff, Ankara, Turkey

    Nazlibilek Sedat

  2. EE Department, Middle East Technical University, Ankara, Turkey

    Erkmen Aydan

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  1. Nazlibilek Sedat
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  2. Erkmen Aydan
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Correspondence to Nazlibilek Sedat.

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Sedat, N., Aydan, E. A fractal conductivity-based approach to mobile sensor networks in a potential field. Int J Adv Manuf Technol 37, 732–746 (2008). https://doi.org/10.1007/s00170-007-1021-0

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  • DOI: https://doi.org/10.1007/s00170-007-1021-0

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