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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]
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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

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