Muestreo adaptativo aplicado a la robótica: Revisión del estado de la técnica

Ignacio Pastor, Joao Valente

Resumen

En este artículo se presenta la revisión de una técnica de muestreo de especial interés para aplicaciones a sistemas roboticos dedicados a la teledetección. Esta técnica es conocida como muestreo adaptativo. En este artículo se realiza una recopilación de las principales técnicas de muestreo adaptativo aplicados a la robótica, haciendo uso de la planificación de trayectorias. Finalmente, se destaca un conjunto de proyectos actualmente en desarrollo, sobre aplicaciones reales de la técnica de muestreo adaptativo en la robótica.

Palabras clave

Robots de exteriores; Muestreo adaptativo; Teledetección; Planificación de trayectorias; Cobertura Óptima

Texto completo:

PDF

Referencias

ASCO, 2016. Autonomous systems, control and optimization lab. http://asco.lcsr.jhu.edu/.

ASTEP, 2016. Astrobiology science and technology for exploring planets program. http://www.frc.ri.cmu.edu/projects/atacama/.

ASTRIL, 2016. Autonomous system technologies research & integration laboratory. http://robotics.asu.edu/.

Béjar, M., Ollero, A., 2008. Modelado y control de helicópteros autónomos. revisión del estado de la técnica. Revista Iberoamericana de Automática e Informática Industrial {RIAI} 5 (4), 5–16.

Bhatta, P., Fiorelli, E., Lekien, F., Leonard, N. E., Paley, D. A., Zhang, F., Bachmayer, R., Sepulchre, R., 2005. Coordination of an underwater glider fleet for adaptive sampling.

Camilli, R., Bingham, B., Jakuba, M., Singh, H., Whelan, J., 2004. Integrating in-situ chemical sampling with auv control systems. In: OCEANS’04. MTTS/IEEE TECHNO-OCEAN’04. Vol. 1. IEEE, pp. 101–109.

Cannell, C. J., Stilwell, D. J., 2005. A comparison of two approaches for adaptive sampling of environmental processes using autonomous underwater vehicles. In: OCEANS, 2005. Proceedings of MTS/IEEE. IEEE, pp. 1514–1521.

Carreras, M., Ridao, P., Garc´ıa, R., Ribas, D., Palomeras, N., 2012. Inspeccion´ visual subacuatica mediante rob ´ otica submarina. ´ Revista Iberoamericana de Automatica ´ e Informatica Industrial ´ {RIAI} 9 (1), 34–45.

Castillo, P., Garc´ıa, P., Lozano, R., Albertos, P., 2007. Modelado y estabilizacion de un helic ´ optero con cuatro rotores. ´ Revista Iberoamericana de Automatica ´ e Informatica Industrial ´ {RIAI} 4 (1), 41–57.

Chaudhuri, A., Stenger, H., 2005. Survey sampling: theory and methods. CRC Press.

Comunidad de Madrid, 2015. Los drones y sus aplicaciones en la ingeniería civil.

Detweiler, C., Ore, J.-P., Anthony, D., Elbaum, S., Burgin, A., Lorenz, A., 9 2015. Environmental reviews and case studies: Bringing unmanned aerial systems closer to the environment. Environmental Practice 17, 188–200.

Dunbabin, M., Marques, L., March 2012. Robots for environmental monitoring: Significant advancements and applications. Robotics Automation Magazine, IEEE 19 (1), 24–39.

Envirobot, 2016. A swimming robot piloted by biological sensors to measure and locate pollutants in aquatic systems. http://wp.unil.ch/envirobot/.

Fiorelli, E., Bhatta, P., Leonard, N. E., Shulman, I., 2003. Adaptive sampling using feedback control of an autonomous underwater glider fleet. In: Proceedings of 13th Int. Symp. on Unmanned Untethered Submersible Technology (UUST).

Fiorelli, E., Leonard, N. E., Bhatta, P., Paley, D. A., Bachmayer, R., Fratantoni, D. M., 2006. Multi-auv control and adaptive sampling in monterey bay. Oceanic Engineering, IEEE Journal of 31 (4), 935–948.

Hombal, V., Sanderson, A., Blidberg, D., Sept 2010. Multiscale adaptive sampling in environmental robotics. In: Multisensor Fusion and Integration for Intelligent Systems (MFI), 2010 IEEE Conference on. pp. 80–87.

Kim, Y.-H., Shell, D., May 2014. Distributed robotic sampling of nonhomogeneous spatio-temporal fields via recursive geometric sub-division. In: Robotics and Automation (ICRA), 2014 IEEE International Conference on. pp. 557–562.

Lermusiaux, P. F., 2007. Adaptive modeling, adaptive data assimilation and adaptive sampling. Physica D: Nonlinear Phenomena 230 (1), 172–196.

Lermusiaux, P. F., Haley Jr, P. J., Yilmaz, N. K., 2007. Environmental prediction, path planning and adaptive sampling: Sensing and modeling for effi- cient ocean monitoring, management and pollution control. Sea Technology 48 (9), 35–38.

Limnobotics, 2016. The autonomous sampling boat. http://limnobotics.ch/en/.

Low, K. H., Gordon, G. J., Dolan, J. M., Khosla, P., 2005. Adaptive sampling for multi-robot wide area prospecting.

Low, K. H., Gordon, G. J., Dolan, J. M., Khosla, P., 2007. Adaptive sampling for multi-robot wide-area exploration. In: Robotics and Automation, 2007 IEEE International Conference on. IEEE, pp. 755–760.

Moreno, H. A., Saltaren, R., Puglisi, L., Carrera, I., Cárdenas, P., Álvarez, C., 2014. Robótica submarina: Conceptos, elementos, modelado y control. Revista Iberoamericana de Automática e Informática Industrial {RIAI} 11 (1), 3–19.

MSEAS, 2016. Multidisciplinary simulation, estimation, and assimilation systems. http://mseas.mit.edu/.

Mysorewala, M., Cheded, L., Popa, D., 2012. A distributed multi-robot adaptive sampling scheme for the estimation of the spatial distribution in widespread fields. EURASIP Journal on Wireless Communications and Networking 2012 (1).

Mysorewala, M., Popa, D., Lewis, F., 2009. Multi-scale adaptive sampling with mobile agents for mapping of forest fires. Journal of Intelligent and Robotic Systems 54 (4), 535–565.

Neumann, P., Asadi, S., Lilienthal, A., Bartholmai, M., Schiller, J., March 2012. Autonomous gas-sensitive microdrone: Wind vector estimation and gas distribution mapping. Robotics Automation Magazine, IEEE 19 (1), 50–61.

NIMBUS, 2016. Nebraska intelligent mobile unmanned systems lab. http://nimbus.unl.edu/.

Ortiz, F., Guerrero, A., Sánchez-Ledesma, F., García-Córdova, F., Alonso, D., Gilabert, J., 2015. Diseño del software de control de un {UUV} para monitorización oceanográfica usando un modelo de componentes y framework con despliegue flexible. Revista Iberoamericana de Automática e Informática Industrial {RIAI} 12 (3), 325–337.

Ouyang, R., Low, K. H., Chen, J., Jaillet, P., 2014. Multi-robot active sensing of non-stationary gaussian process-based environmental phenomena. In: Proceedings of the 2014 international conference on Autonomous agents and multi-agent systems. International Foundation for Autonomous Agents and Multiagent Systems, pp. 573–580.

Popa, D. O., Sanderson, A. C., Komerska, R. J., Mupparapu, S. S., Blidberg, D. R., Chappel, S. G., 2004. Adaptive sampling algorithms for multiple autonomous underwater vehicles. In: Autonomous Underwater Vehicles, 2004 IEEE/OES. IEEE, pp. 108–118.

Prados, R., García, R., Neumann, L., 2013. Construcción automática de ortofotomapas: una aproximación fotométrica. Revista Iberoamericana de Automática e Informática Industrial {RIAI} 10 (1), 104–115.

Rahimi, M., Hansen, M., Kaiser, W. J., Sukhatme, G. S., Estrin, D., 2005. Adaptive sampling for environmental field estimation using robotic sensors. In: Intelligent Robots and Systems, 2005. (IROS 2005). 2005 IEEE/RSJ International Conference on. IEEE, pp. 3692–3698.

Rahimi, M., Pon, R., Kaiser, W. J., Sukhatme, G., Estrin, D., Srivastava, M., 2004. Adaptive sampling for environmental robotics. In: Robotics and Automation, 2004. Proceedings. ICRA’04. 2004 IEEE International Conference on. Vol. 4. IEEE, pp. 3537–3544.

Seber, G. A., Salehi, M. M., 2012. Adaptive sampling designs: inference for sparse and clustered populations. Springer Science & Business Media.

Smith, R. N., Schwager, M., Smith, S. L., Jones, B. H., Rus, D., Sukhatme, G. S., 2011. Persistent ocean monitoring with underwater gliders: Adapting sampling resolution. Journal of Field Robotics 28 (5), 714–741.

Sukhatme, G. S., Dhariwal, A., Zhang, B., Oberg, C., Stauffer, B., Caron, D. A., 2007. Design and development of a wireless robotic networked aquatic microbial observing system. Environmental Engineering Science 24 (2), 205– 215.

Thompson, D. R., Cabrol, N. A., Furlong, M., Hardgrove, C., Low, B. K. H., Moersch, J., Wettergreen, D., May 2013. Adaptive sensing of time series with application to remote exploration. In: Robotics and Automation (ICRA), 2013 IEEE International Conference on. pp. 3463–3468.

Thompson, S., 2011. Adaptive network and spatial sampling. Statistics Canada 37, Supplement (2), 183 – 196.

Thompson, S. K., Collins, L. M., 2002. Adaptive sampling in research on riskrelated behaviors. Drug and Alcohol Dependence 68, Supplement, 57–67.

Yilmaz, N., Evangelinos, C., Lermusiaux, P., Patrikalakis, N., Oct 2008. Path planning of autonomous underwater vehicles for adaptive sampling using mixed integer linear programming. Oceanic Engineering, IEEE Journal of 33 (4), 522–537.

Yilmaz, N. K., 2005. Path planning of autonomous underwater vehicles for adaptive sampling. Ph.D. thesis, Massachusetts Institute of Technology.

Yu, H., Jiao, Y., Su, Z., Reid, K., 2012. Performance comparison of traditional sampling designs and adaptive sampling designs for fishery-independent surveys: A simulation study. Fisheries Research 113 (1), 173–181.

Zarco-Tejada, P. J., Berni, J. A. J., Suarez, L., Fereres, E., 2008. ´ A new era in remote sensing of crops with unmanned robots. SPIE Newsroom.

Zhang, B., Sukhatme, G. S., 2007. Adaptive sampling for estimating a scalar field using a robotic boat and a sensor network. In: Robotics and Automation, 2007 IEEE International Conference on. IEEE, pp. 3673–3680.

Abstract Views

823
Metrics Loading ...

Metrics powered by PLOS ALM


 

Citado por (artículos incluidos en Crossref)

This journal is a Crossref Cited-by Linking member. This list shows the references that citing the article automatically, if there are. For more information about the system please visit Crossref site

1. Comparison of Heuristic Algorithms in Discrete Search and Surveillance Tasks Using Aerial Swarms
Pablo Garcia-Aunon, Antonio Barrientos Cruz
Applied Sciences  vol: 8  num.: 5  primera página: 711  año: 2018  
doi: 10.3390/app8050711



Creative Commons License

Esta revista se publica bajo una Licencia Creative Commons Attribution-NonCommercial-CompartirIgual 4.0 International (CC BY-NC-SA 4.0)

Universitat Politècnica de València     https://doi.org/10.4995/riai

e-ISSN: 1697-7920     ISSN: 1697-7912