Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-25T05:41:10.920Z Has data issue: false hasContentIssue false
  • English
  • Français

Parameter Identification of Spatial–Temporal Varying Processes by a Multi-Robot System in Realistic Diffusion Fields

Published online by Cambridge University Press:  04 September 2020

Wencen Wu Open the ORCID record for Wencen Wu [Opens in a new window] ,
Jie You ,
Yufei Zhang ,
Mingchen Li  and
Kun Su
Wencen Wu*
Affiliation:
San Jose State University, San Jose, CA95192, USA
Jie You
Affiliation:
Rensselaer Polytechnic Institute, Troy, NY12180, USA, E-mails: [email protected], [email protected], [email protected], [email protected]
Yufei Zhang
Affiliation:
Rensselaer Polytechnic Institute, Troy, NY12180, USA, E-mails: [email protected], [email protected], [email protected], [email protected]
Mingchen Li
Affiliation:
Rensselaer Polytechnic Institute, Troy, NY12180, USA, E-mails: [email protected], [email protected], [email protected], [email protected]
Kun Su
Affiliation:
Rensselaer Polytechnic Institute, Troy, NY12180, USA, E-mails: [email protected], [email protected], [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

In this article, we investigate the problem of parameter identification of spatial–temporal varying processes described by a general nonlinear partial differential equation and validate the feasibility and robustness of the proposed algorithm using a group of coordinated mobile robots equipped with sensors in a realistic diffusion field. Based on the online parameter identification method developed in our previous work using multiple mobile robots, in this article, we first develop a parameterized model that represents the nonlinear spatially distributed field, then develop a parameter identification scheme consisting of a cooperative Kalman filter and recursive least square method. In the experiments, we focus on the diffusion field and consider the realistic scenarios that the diffusion field contains obstacles and hazard zones that the robots should avoid. The identified parameters together with the located source could potentially assist in the reconstruction and monitoring of the field. To validate the proposed methods, we generate a controllable carbon dioxide (CO2) field in our laboratory and build a static CO2 sensor network to measure and calibrate the field. With the reconstructed realistic diffusion field measured by the sensor network, a multi-robot system is developed to perform the parameter identification in the field. The results of simulations and experiments show satisfactory performance and robustness of the proposed algorithms.

Keywords

Parameter identificationDistributed parameter systemsSpatial–temporal varying processesMulti-robot systemExperimental validation
Type
Articles
Information
Robotica , Volume 39 , Issue 5 , May 2021 , pp. 842 - 861
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Dunbabin, M. and Marques, L., “Robotics for environmental monitoring: Significant advancements and applications,” IEEE Robot. Autom. Mag. 19(1), 24–39 (2012).CrossRefGoogle Scholar
Liu, Z., “A Supervisory Approach for Hazardous Chemical Source Localization,” 2013 IEEE International Conference on Mechatronics and Automation (ICMA) (2013) pp. 4–7.Google Scholar
Fukazawa, Y. and Ishida, H., “Estimating gas-source location in outdoor environment using mobile robot equipped with gas sensors and anemometer,” IEEE Sens., 1721–1724 (2009).CrossRefGoogle Scholar
Guo, L. Z., Billings, S. A. and Wei, H. L., “Estimation of spatial derivatives and identification of continuous spatio-temporal dynamical systems,” Internal J. Control 79(9), 1118–1135 (2006).CrossRefGoogle Scholar
Yashiro, Y., Eguchi, K., Iwasaki, S., Yamauchi, Y. and Nakata, M., “Development of obstacle avoidance control for robotic products using potential method,” Mitsubishi Heavy Ind. Tech. Rev. 51(1), 34–39 (2014).Google Scholar
Anderson, G., Sheesley, C., Tolson, J., Wilson, E. and Tunstel, E., “A Mobile Robot System for Remote Measurements of Ammonia Vapor in the Atmosphere,” 2006 IEEE International Conference on System, Man and Cybernetics (SMC) (2006) pp. 241–246.Google Scholar
Rossi, L. A., Krishnamachari, B. and Jay Kuo, C.-C., “Distributed Parameter Estimation for Monitoring Diffusion Phenomena Using Physical Models,” 2004 IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks (SECON) (2004) pp. 460–469.Google Scholar
Krstic, M. and Smyshlyaev, A., “Adaptive control of PDEs,” Ann. Rev. Control 32(2), 149–160 (2008).CrossRefGoogle Scholar
Ucinski, D., Optimal Measurement Methods for Distributed Parameter System Identification (CRC Press, Boca Raton, London, New York, Washington, DC., 2004).CrossRefGoogle Scholar
Demetriou, M. A. and Hussein, I. I., “Estimation of spatially distributed processes using mobile spatially distributed sensor network,” SIAM J. Control Optim. 48(1), 266–291 (2009).CrossRefGoogle Scholar
Abdolee, R., Champagne, B. and Sayed, A. H., “Estimation of space-time varying parameters using a diffusion LMS algorithm,” IEEE Trans. Sig. Process. 62(2), 403–418 (2014).CrossRefGoogle Scholar
Li, S., Guo, Y. and Bingham, B., “Multi-robot Cooperative Control for Monitoring and Tracking Dynamic Plumes,” 2014 IEEE International Conference on Robotics and Automation (ICRA) (2014) pp. 67–73.Google Scholar
Bennetts, V. M. H., Lilienthal, A. J., Khaliq, A. A., Sese, V. P. and Trincavelli, M., “Towards Real-World Gas Distribution Mapping and Leak Localization Using a Mobile Robot with 3D and Remote Gas Sensing Capabilities,” IEEE International Conference on Robotics and Automation(ICRA) (2013) pp. 2327–2332.Google Scholar
You, J., Zhang, F. and Wu, W., “Cooperative Filtering for Parameter Identification of Diffusion Processes,” 2016 IEEE Conference on Decision and Control (CDC) (2016) pp. 4327–4333.Google Scholar
Li, M., Su, K., Zhang, Y., You, J. and Wu, W., “Experimental Validation of Diffusion Coefficient Identification Using a Multi-Robot System,” 2016 IEEE MIT Undergraduate Research Technology Conference (URTC) (2016).CrossRefGoogle Scholar
Marques, L., Martins, A. and de Almeida, A. T., “Environmental Monitoring with Mobile Robots,” 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems (2005) pp. 3624–3629.Google Scholar
Ishida, H., Wada, Y. and Matsukura, H., “Chemical sensing in robotic applications: A review,” IEEE Sens. J. 12(11), 3153–3173 (2012).CrossRefGoogle Scholar
Rossi, M. and Brunelli, D., “Autonomous gas detection and mapping with unmanned aerial vehicles,” IEEE Trans. Inst. Meas. 65(4), 765–775 (2016).CrossRefGoogle Scholar
Marjovi, A., Nunes, J., Sousa, P., Faria, R. and Marques, L., “An Olfactory-Based Robot Swarm Navigation Method,” IEEE International Conference on Robotics and Automation (ICRA), vol. 65(4) (2010) pp. 4598–4963.Google Scholar
You, J. and Wu, W., “Sensing-Motion Co-Planning for Reconstructing a Spatially Distributed Field Using a Mobile Sensor Network,” IEEE 56th Conference on Decision and Control (CDC) (2017) pp. 3113–3118.Google Scholar
Ucinski, D. and Patan, M., “Sensor network design for the estimation of spatially distributed processes,” Int. J. Appl. Math. Comput. Sci. 20(3), 459–481 (2010).CrossRefGoogle Scholar
Zhang, F. and Leonard, N. E., “Cooperative control and filtering for cooperative exploration,” IEEE Trans. Autom. Control 55(3), 128–136 (2010).CrossRefGoogle Scholar
Cabrita, G., Marques, L. and Gazi, V., “Virtual Cancelation Plume for Multiple Odor Source Localization,” 2007 IEEE International Conference on Intelligent Robots and Systems (IROS) (2013) pp. 5552–5558.Google Scholar
You, J., Zhang, Y., Li, M., Su, K., Zhang, F. and Wu, W., “Cooperative Parameter Identification of Advection-diffusion Processes Using a Mobile Sensor Network,” 2017 American Control Conference(ACC) (2017) pp. 3230–3236.Google Scholar
Rimon, E. and Koditschek, D. E., “Exact robot navigation using artificial potential functions,” IEEE Trans. Robot. Autom. 8(5), 501–518 (1992).CrossRefGoogle Scholar
Silva-Ortigoza, R., Marquez-Sanchez, C., Carrizosa-Corral, F., Hernandez-Guzman, V. M., Garcia-Sanchez, J. R., Taud, H., Marciano-Melchor, M. and Alvarez-Cedillo, J. A., “Obstacle Avoidance Task for a Wheeled Mobile Robot: A Matlab-Simulink-Based Didactic Application,” In: MATLAB Applications for the Practical Engineer, Chapter 2 (2014) pp.79–102.Google Scholar
Demetriou, M. A., Gatsonis, N. A. and Court, J. R., “Coupled control-computational fluids approach for the estimation of the concentration form a moving gaseous source in a 2-D domain with a Lyapunov-guided sensing aerial vehicle,” IEEE Trans. Control Syst. Tech. 22(3), 853–867 (2014).CrossRefGoogle Scholar
You, J. and Wu, W., “Online passive identifier of spatially distributed systems using mobile sensor networks,” IEEE Trans. Control Syst. Tech. 25(6), 2151–2159 (2017).CrossRefGoogle Scholar
You, J. and Wu, W., “Geometric Reinforcement Learning Based Path Planning for Mobile Sensor Networks in Advection-Diffusion Field Reconstruction,” IEEE 57th Conference on Decision and Control (CDC) (2018) pp. 1949–1954.Google Scholar