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Research Papers

Distributed Full-State Observers With Limited Communication and Application to Cooperative Target Localization

[+] Author and Article Information
Davide Spinello

Department of Mechanical Engineering,
University of Ottawa,
Ottawa, ON K1N 6N5, Canada
e-mail: dspinell@uottawa.ca

Daniel J. Stilwell

The Bradley Department of Electrical &
Computer Engineering,
Virginia Polytechnic Institute & State University,
Blacksburg, VA 24060
e-mail: stilwell@vt.edu

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received March 11, 2013; final manuscript received November 18, 2013; published online March 4, 2014. Assoc. Editor: Sergey Nersesov.

J. Dyn. Sys., Meas., Control 136(3), 031022 (Mar 04, 2014) (14 pages) Paper No: DS-13-1115; doi: 10.1115/1.4026173 History: Received March 11, 2013; Revised November 18, 2013

We present a fully decentralized motion control algorithm for the coordination of platoons of mobile agents with highly restricted communication capabilities. In order to address very low bandwidth communication between agents and time varying communication network topologies, we utilize a distributed full-state observer onboard each agent. Agent motion and data fusion algorithms are implemented locally by each agent based on the state of the local full-state estimator. Although no separation principle exists between decentralized agent motion control and distributed data fusion in general, we introduce a gradient-based framework in which simultaneously we achieve asymptotic agreement among full-state estimators and convergence of desired agent motion.

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References

Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., and Cayirci, E., 2002, “Wireless Sensor Networks: A Survey,” Comput. Netw., 38, pp. 393–422. [CrossRef]
Yick, J., Mukherjee, B., and Ghosal, D., 2008, “Wireless Sensor Network Survey,” Comput. Netw., 52(12), pp. 2292–2330. [CrossRef]
Cao, Y., Yu, W., Ren, W., and Chen, G., 2013, “An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination,” IEEE Trans. Indus. Inform., 9(1), pp. 427–438. [CrossRef]
LigginsM. E., II, Chong, C.-Y., Kadar, I., Alford, M. G., Vannicola, V., and Thomopoulos, S., 1997, “Distributed Fusion Architectures and Algorithms for Target Tracking”. Proc. IEEE, 85(1), pp. 95–107. [CrossRef]
Chung, T. H., Burdick, J. W., and Murray, R. M., 2006, “Decentralized Motion Control of Mobile Sensing Agents in a Network,” Proceedings of the IEEE International Conference on Robotics and Automation.
Chung, T. H., Gupta, V., Burdick, J. W., and Murray, R. M., 2004, “On a Decentralized Active Sensing Strategy Using Mobile Sensor Platforms in a Network,” Proceedings of the IEEE conference on Decision and Control.
Martínez, S., and Bullo, F., 2006, “Optimal Sensor Placement and Motion Coordination for Target Tracking,” Automatica, 42(4), pp. 661–668. [CrossRef]
Yang, P., Freeman, R. A., and Lynch, K. M., 2008, “Multi-Agent Coordination by Decentralized Estimation and Control,” IEEE Trans. Autom. Control, 53(11), pp. 2480–2496. [CrossRef]
Tang, Z., and Ozguner, U., 2008, “Cooperative Sensor Deployment for Multi-Target Monitoring,” Int. J. Robust Nonlinear Control, 18(2), pp. 196–217. [CrossRef]
Shi, G., and Hong, Y., 2009, “Global Target Aggregation and State Agreement of Nonlinear Multi-Agent Systems With Switching Topologies,” Automatica, 45(5), pp. 1165–1175. [CrossRef]
Wang, Z., and Gu, D., 2012, “Cooperative Target Tracking Control of Multiple Robots,” IEEE Trans. Indus. Electron., 59(8), pp. 3232–3240. [CrossRef]
Zhang, C., and Fei, S., 2012, “Energy Efficient Target Tracking Algorithm Using Cooperative Sensors,” J. Syst. Eng. Electron., 23(5), pp. 640–648.
Ramos, H. S., Boukerche, A., Pazzi, R. W., Frery, A. C., and Loureiro, A. A. F., 2012, “Cooperative Target Tracking in Vehicular Sensor Networks,” IEEE Wireless Commun., 19(5), pp. 66–73. [CrossRef]
Chen, Y.-C., and Wen, C.-Y., 2012, “Decentralized Cooperative TOA/AOA Target Tracking for Hierarchical Wireless Sensor Networks,” Sensors, 12(11), pp. 15308–15337. [CrossRef] [PubMed]
Ma, L., and Hovakimyan, N., 2013, “Vision-Based Cyclic Pursuit for Cooperative Target Tracking,” J. Guid. Control Dyn., 36(2), pp. 617–622. [CrossRef]
Belta, C., and Kumar, V., 2004, “Abstraction and Control for Groups of Robots,” IEEE Trans. Rob., 20(5), pp. 865–875. [CrossRef]
Ogren, P., Fiorelli, E., and Leonard, N., 2004, “Cooperative Control of Mobile Sensor Networks: Adaptive Gradient Climbing in a Distributed Environment,” IEEE Trans. Autom. Control, 49(8), pp. 1292–1302. [CrossRef]
Cortés, J., Martínez, S., Karatas, T., and Bullo, F., 2004, “Coverage Control for Mobile Sensing Networks,” IEEE Trans. Rob. Autom., 20(2), pp. 243–255. [CrossRef]
Lekien, F., and Leonard, N. E., 2009, “Nonuniform Coverage and Cartograms,” SIAM J. Control Optim., 48(1), pp. 351–372. [CrossRef]
Fax, J. A., and Murray, R. M., 2004, “Information Flow and Cooperative Control of Vehicle Formations,” IEEE Trans. Autom. Control, 49(9), pp. 1465–1476. [CrossRef]
Freeman, R. A., Yang, P., and Lynch, K. M., 2006, “Distributed Estimation and Control of Swarm Formation Statistics,” Proceedings of the American Control Conference, pp. 749–755.
Laventall, K., and Cortés, J., 2009, “Coverage Control by Multi-Robot Networks With Limited-Range Anisotropic Sensory,” Int. J. Control, 82(6), pp. 1113–1121. [CrossRef]
Jadbabaie, A., Lin, J., and Morse, A., 2003, “Coordination of Groups of Mobile Autonomous Agents Using Nearest Neighbor Rules,” IEEE Trans. Autom. Control, 48(6), pp. 988–1001. [CrossRef]
Ren, W., and Atkins, E., 2007, “Distributed Multi-Vehicle Coordinated Control Via Local Information Exchange,” Int. J. Robust Nonlinear Control, 17(10–11), pp. 1002–1033. [CrossRef]
Cao, Y., and Ren, W., 2010, “Multi-Vehicle Coordination for Double-Integrator Dynamics Under Fixed Undirected/Directed Interaction in a Sampled-Data Setting,” Int. J. Robust Nonlinear Control, 20(9, SI), pp. 987–1000.
Kansal, A., Kaiser, W., Pottie, G., Srivastava, M., and Sukhatme, G., 2007, “Reconfiguration Methods for Mobile Sensor Networks,” ACM Trans. Sens. Netw., 3(4). p. 1281497. [CrossRef]
Zou, Y., and Pagilla, P. R., 2009, “Distributed Constraint Force Approach for Coordination of Multiple Mobile Robots,” J. Intell. Robotic Syst., 56(1–2), pp. 5–21. [CrossRef]
Carli, R., and Bullo, F., 2009, “Quantized Coordination Algorithms for Rendezvous and Deployment,” SIAM J. Control Optim., 48(3), pp. 1251–1274. [CrossRef]
Ögren, P., Fiorelli, E., and Leonard, N. E., 2004, “Cooperative Control of Mobile Sensor Networks: Adaptive Gradient Climbing in a Distributed Environment,” IEEE Trans. Autom. Control49(8), pp. 1292–1302. [CrossRef]
Porfiri, M., Roberson, D. G., and Stilwell, D. J., 2007, “Tracking and Formation Control of Multiple Autonomous Agents: A Two-Level Consensus Approach,” Automatica, 43(8), pp. 1318–1328. [CrossRef]
Simic, S., and Sastry, S., 2003, “Distributed Environmental Monitoring Using Random Sensor Networks,” Proceeding of the 2nd International Workshop on Information Processing in Sensor Networks, pp. 582–592.
Susca, S., Bullo, F., and Martinez, S., 2008, “Monitoring Environmental Boundaries With a Robotic Sensor Network,” IEEE Trans. Control Syst. Technol., 16(2), pp. 288–296. [CrossRef]
Ren, W., and Cao, Y., 2011, Distributed Coordination of Multi-agent Networks: Emergent Problems, Models, and Issues, Communications and Control Engineering. Springer-Verlag, London.
Gadre, A. S., Maczka, D. K., Spinello, D., McCarter, B. R., Stilwell, D. J., Neu, W., Roan, M. J., and Hennage, J. B., 2008, “Cooperative Localization of an Acoustic Source Using Towed Hydrophone Arrays,” 2008 IEEE/OES Autonmous Underwater Vehicles, IEEE/OES Autonomous Underwater Vehicles, IEEE; OES, pp. 64–71. IEEE/OES Autonomous Underwater Vehicles Conference (AUV 2008), Woods Hole, MA, OCT 13-14, 2008.
Sepulchre, R., Paley, D. A., and Leonard, N. E., 2007, “Stabilization of Planar Collective Motion: All-To-All Communication,” IEEE Trans. Autom. Control, 52, pp. 811–824. [CrossRef]
Cao, Y., Ren, W., and Li, Y., 2009, “Distributed Discrete-Time Coordinated Tracking With a Time-Varying Reference State and Limited Communication,” Automatica, 45(5), pp. 1299–1305. [CrossRef]
Kar, S., and Moura, J. M. F., 2013, “Consensus Plus Innovations Distributed Inference Over Networks,” IEEE Signal Process. Mag., 30(3), pp. 99–109. [CrossRef]
Bajovic, D., Jakovetic, D., Xavier, J., Sinopoli, B., and Moura, J. M. F., 2011. “Distributed Detection via Gaussian Running Consensus: Large Deviations Asymptotic Analysis,” IEEE Trans. Signal Process., 59(9), pp. 4381–4396. [CrossRef]
Kar, S., and Moura, J. M. F., 2011, “Gossip and Distributed Kalman Filtering: Weak Consensus Under Weak Detectability,” IEEE Trans. Signal Process., 59(4), pp. 1766–1784. [CrossRef]
Abaid, N., and Porfiri, M., 2012, “Leader-Follower Consensus Over Numerosity-Constrained Random Networks,” Automatica, 48(8), pp. 1845–1851. [CrossRef]
Abaid, N., Igel, I., and Porfiri, M., 2012, “On the Consensus Protocol of Conspecific Agents,” Linear Algebra Appl.437(1), pp. 221–235. [CrossRef]
Abaid, N., and Porfiri, M., 2011, “Consensus Over Numerosity-Constrained Random Networks,” IEEE Trans. Autom. Control, 56(3), pp. 649–654. [CrossRef]
Silva Pereira, S., Lopez-Valcarce, R., and Pages-Zamora, A., 2013, “A Diffusion-Based EM Algorithm for Distributed Estimation in Unreliable Sensor Networks,” IEEE Signal Process. Lett.20(6), pp. 595–598. [CrossRef]
Wang, H., Liao, X., and Huang, T., 2013, “Average Consensus in Sensor Networks Via Broadcast Multi-Gossip Algorithms,” Neurocomputing, 117, pp. 150–160. [CrossRef]
Ni, W., Wang, X., and Xiong, C., 2013, “Consensus Controllability, Observability and Robust Design for Leader-Following Linear Multi-Agent Systems,” Automatica, 49(7), pp. 2199–2205. [CrossRef]
Shimizu, K., 2000, “Nonlinear State Observers by the Gradient Descent Method,” Proceedings of the IEEE International Conference on Control Applications, pp. 616–622.
Farina, A., 1999, “Target Tracking With Bearings-Only Measurements,” Signal Process., 78, pp. 61–78. [CrossRef]
Logothetis, A., Isaksson, A., and Evans, R. J., 1997, “An Information Theoretic Approach to Observer Path Design for Bearings-Only Tracking,” Proceedings of the 36th Conference on Decision and Control, pp. 3132–3137.
Spinello, D., and Stilwell, D., “Nonlinear Estimation With State-Dependent Gaussian Observation Noise,” IEEE Trans. Autom. Control, 55(6), pp. 1358–1366. [CrossRef]
Spinello, D., and Stilwell, D. J., 2008, “Nonlinear Estimation With State-Dependent Gaussian Observation Noise,” Tech. Rep. 2, VaCAS.
Bar-Shalom, Y., and Fortman, T. E., 1988, Tracking and Data Association, Academic Press, San Diego, CA.
Bertsekas, D. P., and Tsitsiklis, J. N., 1989, Parallel and Distributed Computation: Numerical Methods, Prentice-Hall, Englewood Cliffs, New Jersey.
Bernstein, D. S., 2005, Matrix Mathematics, Princeton University Press, Princeton, NJ.
Porfiri, M., Roberson, D., and Stilwell, D., 2008, “Fast Switching Analysis of Linear Switched Systems Using Exponential Splitting,” SIAM J. Control Optim., 47(5), pp. 2582–2597. [CrossRef]
Jadbabaie, A., Lin, J., and Morse, A. S., 2003, “Coordination of Groups of Mobile Autonomous Agents Using Nearest Neighbor Rules, IEEE Trans. Autom. Control48(6), pp. 988–1001. [CrossRef]
Ren, W., and Beard, R. W., 2005, “Consensus Seeking in Multiagent Systems Under Dynamically Changing Interaction Topologies,” IEEE Trans. Autom. Control, 50(5), pp. 655–661. [CrossRef]
Kolmogorov, A. N., and Fomin, S. V., 1975, Introductory Real Analysis, Dover Publications, Inc., New York.
Yang, P., Freeman, R. A., and Lynch, K. M., 2007, “Distributed Cooperative Active Sensing Using Consensus Filters,” Proceedings of the IEEE International Conference on Robotics and Automation.
Mutambara, A. G. O., 1998, Decentralized Estimation and Control for Multisensor Systems, CRC Press, Boca Raton, FL.
Fedorov, V., 1972, Theory of Optimal Experiments, Academic Press, New York.
Mihaylova, L., Lefebvre, T., Bruyninckx, H., Gadeyne, K., and Shutter, J. D., 2002, “Active Sensing for Robotics—A Survey,” International Conference on Numerical Methods and Applications, pp. 316–324.
Bowen, R. M., and Wang, C.-C., 1976, Introduction to Vectors and Tensors—Vector and Tensor Analysis, Vol. 2, Plenum Press, New York.
Hermann, R., and Krener, A. R., 1977, “Nonlinear Controllability and Observability”. IEEE Transactions on Automatic Control, AC-22(5), pp. 728–740. [CrossRef]

Figures

Grahic Jump Location
Fig. 1

Schematics of the networked system with intermittent communications and symbols used to describe it. Each agent maintains a local representation of the states of all the other agents in the group. Self representations of the states correspond to true states. Symbols aij are indicators to represent the existence of a link among two nodes at a given time. Through directed communication events the agents share information in the form of a function γ of their true state.

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Fig. 2

Snapshot of the geometric configuration of a moving sensor

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Fig. 3

Trajectories of sensors estimating the position of a static target, with output equal to the sensors position

Grahic Jump Location
Fig. 4

Time history of ∑i=13 cos 2βi[k] (solid line) and ∑i=13 sin 2βi[k] (dashed line)

Grahic Jump Location
Fig. 5

Time history of the functions J(q∧i) in Eq. (36) for three sensors with output equal to the sensor position

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Fig. 6

Time history of the error in the estimation of the state of sensor starting at (−20 m, −15 m)

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Fig. 7

Trajectories of sensors estimating the position of a static target, with output equal to the relative distance to the target

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Fig. 8

Time histories of the functions J(q∧i) in Eq. (36) for three sensors with output equal to the relative distance to the target

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Fig. 9

Time history of the error in the output estimation of sensor starting at (−20 m, −15 m)

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Fig. 10

Time histories of the functions J(q∧i) in Eq. (36) for three sensors with output equal to the state of the sensor and circulant communication events every twenty time steps

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Fig. 11

Time histories of the errors in the estimation of the state of sensor starting at (−20 m, −15 m) with circulant communication events every twenty time steps

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Fig. 12

Time histories of the target state estimates x∧i for three mobile sensors sharing information every twenty updating time steps. The dashed line represents the true state of the target

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Fig. 13

Trajectories of sensors estimating the position of a static target, with output equal to the state of the sensor. Solid lines refer to trajectories generated by the distributed observer proposed here, and dashed lines refer to trajectories generated by the same gradient descent feedback control with complete communication network.

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