Peer-to-peer model for the area coverage and cooperative control of mobile sensor networks

This paper presents a novel model and distributed algorithms for the cooperation and redeployment of mobile sensor networks. A mobile sensor network composes of a collection of wireless connected mobile robots equipped with a variety of sensors. In such a sensor network, each mobile node has sensing, computation, communication, and locomotion capabilities. The locomotion ability enhances the autonomous deployment of the system. The system can be rapidly deployed to hostile environment, inaccessible terrains or disaster relief operations. The mobile sensor network is essentially a cooperative multiple robot system. This paper first presents a peer-to-peer model to define the relationship between neighboring communicating robots. Delaunay Triangulation and Voronoi diagrams are used to define the geometrical relationship between sensor nodes. This distributed model allows formal analysis for the fusion of spatio-temporal sensory information of the network. Based on the distributed model, this paper discusses a fault tolerant algorithm for autonomous self-deployment of the mobile robots. The algorithm considers the environment constraints, the presence of obstacles and the nonholonomic constraints of the robots. The distributed algorithm enables the system to reconfigure itself such that the area covered by the system can be enlarged. Simulation results have shown the effectiveness of the distributed model and deployment algorithms.

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

[2]  J. A. Fax.  Optimal and Cooperative Control of Vehicle Formations , 2002 .

[3]  Vijay Kumar,et al.  Modeling and control of formations of nonholonomic mobile robots , 2001, IEEE Trans. Robotics Autom..

[4]  Miodrag Potkonjak,et al.  Localized algorithms in wireless ad-hoc networks: location discovery and sensor exposure , 2001, MobiHoc '01.

[5]  Qiang Du,et al.  Centroidal Voronoi Tessellations: Applications and Algorithms , 1999, SIAM Rev..

[6]  Xiang-Yang Li,et al.  Coverage in wireless ad-hoc sensor networks , 2002, 2002 IEEE International Conference on Communications. Conference Proceedings. ICC 2002 (Cat. No.02CH37333).

[7]  Jindong Tan,et al.  Coordination of human and mobile manipulator formation in a perceptive reference fame , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[8]  S.X. Yang,et al.  Tracking control of a nonholonomic mobile robot by integrating feedback and neural dynamics techniques , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[9]  Daniel E. Koditschek,et al.  Sequential Composition of Dynamically Dexterous Robot Behaviors , 1999, Int. J. Robotics Res..

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

[11]  Xiang-Yang Li,et al.  Localized Delaunay Triangulation with Application in Ad Hoc Wireless Networks , 2003, IEEE Trans. Parallel Distributed Syst..

[12]  Gaurav S. Sukhatme,et al.  An Incremental Self-Deployment Algorithm for Mobile Sensor Networks , 2002, Auton. Robots.

[13]  Jie Lin,et al.  Coordination of groups of mobile autonomous agents using nearest neighbor rules , 2003, IEEE Trans. Autom. Control..

[14]  V. Kumar,et al.  Ad hoc networks for localization and control , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[15]  Kostas J. Kyriakopoulos,et al.  Navigation of nonholonomic vehicles in complex environments with potential fields and tracking , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[16]  Lynne E. Parker,et al.  Distributed Algorithms for Multi-Robot Observation of Multiple Moving Targets , 2002, Auton. Robots.

[17]  M. Matarić.  Behavior-based robotics as a tool for synthesis of artificial behavior and analysis of natural behavior , 1998, Trends in Cognitive Sciences.

[18]  Alan F. T. Winfield,et al.  Distributed Sensing and Data Collection via Broken Ad Hoc Wireless Connected Networks of Mobile Robots , 2000, DARS.

[19]  Tucker R. Balch,et al.  Behavior-based formation control for multirobot teams , 1998, IEEE Trans. Robotics Autom..

[20]  A. Hodgkin,et al.  A quantitative description of membrane current and its application to conduction and excitation in nerve , 1952, The Journal of physiology.

[21]  Stephen Grossberg,et al.  Nonlinear neural networks: Principles, mechanisms, and architectures , 1988, Neural Networks.

[22]  David A. Schoenwald,et al.  Decentralized control of cooperative robotic vehicles: theory and application , 2002, IEEE Trans. Robotics Autom..

[23]  Rodney A. Brooks,et al.  A Robust Layered Control Syste For A Mobile Robot , 2022 .

[24]  O. Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[25]  Camillo J. Taylor,et al.  A vision-based formation control framework , 2002, IEEE Trans. Robotics Autom..

[26]  Kostas J. Kyriakopoulos,et al.  Sensor-based self-localization for wheeled mobile robots , 1995, J. Field Robotics.

[27]  David W. Payton,et al.  Pheromone Robotics , 2001, Auton. Robots.

[28]  Mark de Berg,et al.  Computational geometry: algorithms and applications , 1997 .