A Study on Swarm Robot-Based Invader-Enclosing Technique on Multiple Distributed Object Environments

Interest about social security has recently increased in favor of safety for infrastructure. In addition, advances in computer vision and pattern recognition research are leading to video-based surveillance systems with improved scene analysis capabilities. However, such video surveillance systems, which are controlled by human operators, cannot actively cope with dynamic and anomalous events, such as having an invader in the corporate, commercial, or public sectors. For this reason, intelligent surveillance systems are increasingly needed to provide active social security services. In this study, we propose a core technique for intelligent surveillance system that is based on swarm robot technology. We present techniques for invader enclosing using swarm robots based on multiple distributed object environment. The proposed methods are composed of three main stages: location estimation of the object, specified object tracking, and decision of the cooperative behavior of the swarm robots. By using particle filter, object tracking and location estimation procedures are performed and a specified enclosing point for the swarm robots is located on the interactive positions in their coordinate system. Furthermore, the cooperative behaviors of the swarm robots are determined via the result of path navigation based on the combination of potential field and wall-following methods. The results of each stage are combined into the swarm robot-based invader-enclosing technique on multiple distributed object environments. Finally, several simulation results are provided to further discuss and verify the accuracy and effectiveness of the proposed techniques.

[1]  Andreas F. Molisch,et al.  Localization via Ultra- Wideband Radios , 2005 .

[2]  D.M. Bevly,et al.  Harmonic potential field path planning for high speed vehicles , 2008, 2008 American Control Conference.

[3]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .

[4]  Greg Welch,et al.  Welch & Bishop , An Introduction to the Kalman Filter 2 1 The Discrete Kalman Filter In 1960 , 1994 .

[5]  Hiroaki Yamaguchi,et al.  A Cooperative Hunting Behavior by Mobile-Robot Troops , 1999, Int. J. Robotics Res..

[6]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[7]  Suprakash Datta,et al.  Localization in wireless sensor networks , 2007, IPSN.

[8]  Frank Allgöwer,et al.  A Nonlinear Synchronization Scheme for Hindmarsh-Rose Models , 2010 .

[9]  F. Gustafsson,et al.  Mobile positioning using wireless networks: possibilities and fundamental limitations based on available wireless network measurements , 2005, IEEE Signal Processing Magazine.

[10]  Jing Ren,et al.  Modified Newton's method applied to potential field-based navigation for mobile robots , 2006, IEEE Transactions on Robotics.

[11]  李幼升,et al.  Ph , 1989 .

[12]  A. Sabanovic,et al.  Basic configuration for mobile robots , 2003, IEEE International Conference on Industrial Technology, 2003.

[13]  I. Yoshihara,et al.  Cooperative behavior of interacting robots , 1998, Artificial Life and Robotics.

[14]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[15]  Vincent W. S. Wong,et al.  Dual and Mixture Monte Carlo Localization Algorithms for Mobile Wireless Sensor Networks , 2007, 2007 IEEE Wireless Communications and Networking Conference.

[16]  Fredrik Gustafsson,et al.  Mobile Positioning Using Wireless Networks , 2005 .

[17]  Ashok K. Agrawala,et al.  Horus: a wlan-based indoor location determination system , 2004 .

[18]  Histogram-Based Color Image Retrieval Psych 221 / EE 362 Project Report , 2008 .

[19]  Wanming Chen,et al.  Particle filtering for range-based localization in Wireless Sensor Networks , 2008, 2008 7th World Congress on Intelligent Control and Automation.

[20]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[21]  Wei Guo,et al.  Signal Processing Techniques in Network- Aided Positioning , 2005 .

[22]  A.H. Sayed,et al.  Network-based wireless location: challenges faced in developing techniques for accurate wireless location information , 2005, IEEE Signal Processing Magazine.

[23]  Yuan-Hsiang Lin,et al.  A wireless PDA-based physiological monitoring system for patient transport , 2004, IEEE Transactions on Information Technology in Biomedicine.

[24]  W. G. Madow.  On the Theory of Systematic Sampling, II , 1944 .

[25]  Barry E. Mullins,et al.  Toward finding an universal search algorithm for swarm robots , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[26]  E.J. Silva,et al.  Robot navigation based on electrostatic field computation , 2006, IEEE Transactions on Magnetics.

[27]  Hyun-Ju Chung,et al.  Comparison of Three Modeling Methods for Identifying Unknown Magnetization of Ferromagnetic Thin Plate , 2011 .

[28]  Yoram Koren,et al.  Potential field methods and their inherent limitations for mobile robot navigation , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[29]  Jitendra Malik,et al.  Robust Multiple Car Tracking with Occlusion Reasoning , 1994, ECCV.