Abstract
This paper covers the use of an adaptive sampling strategy on a sensor network for the estimation of the pose of a remotely controlled mobile robot. The more measurements can be taken, the better the estimation becomes, but if some level of uncertainty is tolerable, the amount of measurements can be significantly reduced. The proposed adaptive sampling technique is triggered by the estimation error covariance matrix, computed by Kalman filter algorithms. This sampling strategy leads to the minimum amount of measurements needed to achieve a certain estimation accuracy, reducing the load on important resources such as computational power and network communications. An example of use is given as the pose estimation of a wheeled robot remotely controlled. A network of camera sensors provides pose measurements allowing the application of an Unscented Kalman Filter estimator in the remote centre.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Mallick, M., Coraluppi, S., Carthel, C.: Advances in asynchronous and decentralized estimation. In: IEEE Proceedings of the Aerospace Conference, vol. 4, pp. 4/1873–4/1888 (2001)
Hespanha, J., Naghshtabrizi, P., Xu, Y.: A survey of recent results in networked control systems. Proceedings of the IEEE 95(1), 138–162 (2007)
Tabuada, P.: Event-triggered real-time scheduling of stabilizing control tasks. IEEE Transactions on Automatic Control 52(9), 1680–1685 (2007)
Mazo Jr., M., Anta, A., Tabuada, P.: On self-triggered control for linear systems: Guarantees and complexity. In: European Control Conference (2009)
Battistelli, G., Benavoli, A., Chisci, L.: Data-driven communication for state estimation with sensor networks. Automatica 48(5), 926–935 (2012)
Sánchez, J., Guarnes, M.A., Dormido, S.: On the application of different event-based sampling strategies to the control of a simple industrial process. Sensors 9(9), 6795–6818 (2009)
Sijs, J., Lazar, M.: Event based state estimation with time synchronous updates. IEEE Transactions on Automatic Control 57(10), 2650–2655 (2012)
Miskowicz, M.: Send-on-delta concept: An event-based data reporting strategy. Sensors 6(1), 49–63 (2006)
Kamen, E., Su, J.: Introduction to Optimal Estimation. Advanced Textbooks in Control and Signal Processing. Springer, London (1999)
Simon, D.: Optimal State Estimation: Kalman, H ∞ , and Nonlinear Approaches. Wiley (2006)
Duton, K., Thompson, S., Barraclough, B.: The art of control engineering. Addison-Wesley (1997)
Julier, S., Uhlmann, J.: Unscented filtering and nonlinear estimation. Proceedings of the IEEE 92(3), 401–422 (2004)
Santos, C., Mazo Jr., M., Espinosa, F.: Adaptive self-triggered control of a remotely operated robot. In: Herrmann, G., Studley, M., Pearson, M., Conn, A., Melhuish, C., Witkowski, M., Kim, J.-H., Vadakkepat, P. (eds.) TAROS-FIRA 2012. LNCS, vol. 7429, pp. 61–72. Springer, Heidelberg (2012)
Levine, W.: The control handbook. IEEE-Press (1996)
Lee, J.H., Hashimoto, H.: Intelligent space. In: Proceedings of the 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000), vol. 2, pp. 1358–1363 (2000)
Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Martínez, M., Espinosa, F., Gardel, A., Santos, C., García, J. (2014). Pose Estimation of a Mobile Robot Based on Network Sensors Adaptive Sampling. In: Armada, M., Sanfeliu, A., Ferre, M. (eds) ROBOT2013: First Iberian Robotics Conference. Advances in Intelligent Systems and Computing, vol 253. Springer, Cham. https://doi.org/10.1007/978-3-319-03653-3_41
Download citation
DOI: https://doi.org/10.1007/978-3-319-03653-3_41
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03652-6
Online ISBN: 978-3-319-03653-3
eBook Packages: EngineeringEngineering (R0)