Deployment and Localization for Mobile Sensor Networks

This paper briefly sketches a pair of algorithms for deploying and localizing a mobile sensor network. We use the term ’mobile sensor network’ to describe a distributed collection of nodes, each of which has sensing, computation, communication and locomotion capabilities. For deployment, we have developed a potential-field-based approach that ensures that any compact initial configuration of nodes will spread out such that the area ’covered’ by the network is maximized. For localization, we have developed an approach that makes use of the nodes themselves as landmarks. Through a combination of maximum likelihood estimation and numerical o13timization, we can, for each node, estimate the relative range, bearing and orientation of every other node in the network. This paper sketches the basic formalism behind these algorithms, and present some experimental results.

[1]  Ronald C. Arkin,et al.  Motor Schema — Based Mobile Robot Navigation , 1989, Int. J. Robotics Res..

[2]  Gaurav S. Sukhatme,et al.  Localization for Mobile Robot Teams: A Maximum Likelihood Approach , 2001 .

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

[4]  Gregory Dudek,et al.  Multi-Robot Exploration of an Unknown Environment, Efficiently Reducing the Odometry Error , 1997, IJCAI.

[5]  Matteo Golfarelli,et al.  Elastic correction of dead-reckoning errors in map building , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

[6]  Wolfram Burgard,et al.  Collaborative multi-robot exploration , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[7]  Sebastian Thrun,et al.  A Probabilistic On-Line Mapping Algorithm for Teams of Mobile Robots , 2001, Int. J. Robotics Res..

[8]  BurgardWolfram,et al.  A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots , 1998 .

[9]  Wolfram Burgard,et al.  Robust Monte Carlo localization for mobile robots , 2001, Artif. Intell..

[10]  Wolfram Burgard,et al.  A Probabilistic Approach to Collaborative Multi-Robot Localization , 2000, Auton. Robots.

[11]  Frank E. Schneider,et al.  Motion Coordination in Formations of Multiple Mobile Robots Using a Potential Field Approach , 2000, DARS.

[12]  Wolfram Burgard,et al.  Coordination for Multi-Robot Exploration and Mapping , 2000, AAAI/IAAI.

[13]  Reid G. Simmons,et al.  Probabilistic Robot Navigation in Partially Observable Environments , 1995, IJCAI.

[14]  W. Burgard,et al.  Markov Localization for Mobile Robots in Dynamic Environments , 1999, J. Artif. Intell. Res..

[15]  Hugh F. Durrant-Whyte,et al.  Mobile robot localization by tracking geometric beacons , 1991, IEEE Trans. Robotics Autom..

[16]  Tucker R. Balch,et al.  Behavior-based coordination of large-scale robot formations , 2000, Proceedings Fourth International Conference on MultiAgent Systems.

[17]  Stergios I. Roumeliotis,et al.  Collective localization: a distributed Kalman filter approach to localization of groups of mobile robots , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[18]  Ryo Kurazume,et al.  An Experimental Study of a Cooperative Positioning System , 2000, Auton. Robots.

[19]  Alan C. Schultz,et al.  Mobile robot exploration and map-building with continuous localization , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[20]  Evangelos E. Milios,et al.  Globally Consistent Range Scan Alignment for Environment Mapping , 1997, Auton. Robots.

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

[22]  Douglas W. Gage,et al.  Command Control for Many-Robot Systems , 1992 .

[23]  Wolfram Burgard,et al.  A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots , 1998, Machine Learning.

[24]  R. Simmons,et al.  Probabilistic Navigation in Partially Observable Environments , 1995 .

[25]  Hugh F. Durrant-Whyte,et al.  A solution to the simultaneous localization and map building (SLAM) problem , 2001, IEEE Trans. Robotics Autom..

[26]  Stephen R. Marsland,et al.  Learning globally consistent maps by relaxation , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[27]  Josep Amat,et al.  Map Generation by Cooperative Low-Cost Robots in Structured Unknown Environments , 1998, Auton. Robots.

[28]  Gaurav S. Sukhatme,et al.  Landmark-based Matching Algorithm for Cooperative Mapping by Autonomous Robots , 2000, DARS.

[29]  Gaurav S. Sukhatme,et al.  Relaxation on a mesh: a formalism for generalized localization , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).