Observability in topology-constrained multi-robot target tracking

In this paper, we consider the problem of controlling a multi-robot team in order to track a mobile target with unknown dynamics. Our contribution in this context is a non-linear observability analysis of the target tracking network, which yields insights into the topological and actuation conditions necessary for the underlying state estimation problem. We demonstrate a metric of observability which allows us to determine robot inputs that maximize observability to improve team localization. Combining the observability metric with topological control then yields a robust target tracking solution. We close the paper with a simulation example which demonstrates the ability of our solution to cope with scenarios in which relative measurements poorly distinguish the target.

[1]  António Manuel Santos Pascoal,et al.  An Observability Metric for Underwater Vehicle Localization Using Range Measurements , 2013, Sensors.

[2]  Gaurav S. Sukhatme,et al.  Constrained Interaction and Coordination in Proximity-Limited Multiagent Systems , 2013, IEEE Transactions on Robotics.

[3]  Gaurav S. Sukhatme,et al.  Cooperative Control for Target Tracking with Onboard Sensing , 2014, ISER.

[4]  David C. Lay,et al.  Linear Algebra and Its Applications, 4th Edition , 1994 .

[5]  Ba-Tuong Vo,et al.  Sensor management for multi-target tracking via multi-Bernoulli filtering , 2013, Autom..

[6]  Brian D. O. Anderson,et al.  A Theory of Network Localization , 2006, IEEE Transactions on Mobile Computing.

[7]  Dimitrios G. Kottas,et al.  Camera-IMU-based localization: Observability analysis and consistency improvement , 2014, Int. J. Robotics Res..

[8]  Daniel J. Stilwell,et al.  Underwater navigation in the presence of unknown currents based on range measurements from a single location , 2005 .

[9]  G. Laman On graphs and rigidity of plane skeletal structures , 1970 .

[10]  Audra E. Kosh,et al.  Linear Algebra and its Applications , 1992 .

[11]  Gaurav S. Sukhatme,et al.  Distributed combinatorial rigidity control in multi-agent networks , 2013, 52nd IEEE Conference on Decision and Control.

[12]  Gaurav S. Sukhatme,et al.  Evaluating Network Rigidity in Realistic Systems: Decentralization, Asynchronicity, and Parallelization , 2014, IEEE Transactions on Robotics.

[13]  Arthur J. Krener,et al.  Measures of unobservability , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[14]  Mehran Mesbahi,et al.  On the observability properties of homogeneous and heterogeneous networked dynamic systems , 2008, 2008 47th IEEE Conference on Decision and Control.

[15]  Reza Olfati-Saber,et al.  Collaborative target tracking using distributed Kalman filtering on mobile sensor networks , 2011, Proceedings of the 2011 American Control Conference.

[16]  Vijay Kumar,et al.  Approximate representations for multi-robot control policies that maximize mutual information , 2014, Robotics: Science and Systems.

[17]  David W. Lewis,et al.  Matrix theory , 1991 .

[18]  A. Krener,et al.  Nonlinear controllability and observability , 1977 .

[19]  W. Whiteley,et al.  Generating Isostatic Frameworks , 1985 .