Battery Level Estimation of Mobile Agents under Communication Constraints

Consider a team of mobile agents monitoring large areas, e.g. in the ocean or the atmosphere, with limited sensing resources. Only the leader transmits information to other agents, and the leader has a role to monitor battery levels of all other agents. Every now and then, the leader commands all other agents to move toward or away from the leader with speeds proportional to their battery levels. The leader then simultaneously estimates the battery levels of all other agents from measurements of the relative distances between the leader and other agents. We propose a nonlinear system model that integrates a particle motion model and a dynamic battery model that has demonstrated high accuracy in battery capacity prediction. The extended Kalman filter (EKF) is applied to this nonlinear model to estimate the battery level of each agent. We improve the EKF so that, in addition to gain optimization embedded in the EKF, the motions of agents are controlled to minimize estimation error. Simulation results are presented to demonstrate effectiveness of the proposed method.

[1]  Naomi Ehrich Leonard,et al.  Coordination of an underwater glider fleet for adaptive sampling , 2005 .

[2]  F. Allgöwer,et al.  Nonlinear Model Predictive Control: From Theory to Application , 2004 .

[3]  Frank Allgöwer,et al.  Nonlinear model predictive control : towards new challenging applications , 2009 .

[4]  Sujit Dey,et al.  Battery life estimation of mobile embedded systems , 2001, VLSI Design 2001. Fourteenth International Conference on VLSI Design.

[5]  F. Bullo,et al.  Motion Coordination with Distributed Information , 2007 .

[6]  Fumin Zhang,et al.  A Dynamic Battery Model for Co-design in Cyber-Physical Systems , 2009, 2009 29th IEEE International Conference on Distributed Computing Systems Workshops.

[7]  A.S. Morse,et al.  Information structures to secure control of rigid formations with leader-follower architecture , 2005, Proceedings of the 2005, American Control Conference, 2005..

[8]  M. Egerstedt,et al.  Leader-based multi-agent coordination: controllability and optimal control , 2006, 2006 American Control Conference.

[9]  Naomi Ehrich Leonard,et al.  Coordinated patterns of unit speed particles on a closed curve , 2007, Syst. Control. Lett..

[10]  Sarma Vrudhula,et al.  A model for battery lifetime analysis for organizing applications on a pocket computer , 2003, IEEE Trans. Very Large Scale Integr. Syst..

[11]  L. Magni,et al.  Lecture Notes in Control and Information Sciences: Preface , 2009 .

[12]  Ramesh R. Rao,et al.  Energy efficient battery management , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[13]  Vijay Kumar,et al.  Leader-to-formation stability , 2004, IEEE Transactions on Robotics and Automation.

[14]  John H. Reif,et al.  Synthesis of Parallel Algorithms , 1993 .

[15]  Naomi Ehrich Leonard,et al.  Cooperative Filters and Control for Cooperative Exploration , 2010, IEEE Transactions on Automatic Control.

[16]  Giancarlo Ferrari-Trecate,et al.  Containment Control in Mobile Networks , 2008, IEEE Transactions on Automatic Control.

[17]  Naomi Ehrich Leonard,et al.  Control of coordinated patterns for ocean sampling , 2007, Int. J. Control.

[18]  Magnus Egerstedt,et al.  Distributed Coordination Control of Multiagent Systems While Preserving Connectedness , 2007, IEEE Transactions on Robotics.