We investigate the problem of simultaneous parameter identification and mapping of a spatially distributed field using a mobile sensor network. We first develop a parametrized model that represents the spatially distributed field. Based on the model, a recursive least squares algorithm is developed to achieve online parameter identification. Next, we design a global state observer, which uses the estimated parameters, together with data collected by the mobile sensor network, to real-timely reconstruct the whole spatial-temporal varying field. Since the performance of the parameter identification and map reconstruction algorithms depends on the trajectories of the mobile sensors, we further develop a Lyapunov redesign based online trajectory planning algorithm for the mobile sensor network so that the mobile sensors can use local real-time information to guide them to move along information-rich paths that can improve the performance of the parameter identification and map construction. Lastly, a cooperative filtering scheme is developed to provide the state estimates of the spatially distributed field, which enables the recursive least squares method. To test the proposed algorithms in realistic scenarios, we first build a CO2 diffusion field in a lab and construct a sensor network to measure the field concentration over time. We then validate the algorithms in the reconstructed CO2 field in simulation. Simulation results demonstrate the efficiency of the proposed method.
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Parameter Identification of Spatial–Temporal Varying Processes by a Multi-Robot System in Realistic Diffusion Fields
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 (CO 2 ) field in our laboratory and build a static CO 2 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.
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- Award ID(s):
- 1917300
- NSF-PAR ID:
- 10288744
- Date Published:
- Journal Name:
- Robotica
- Volume:
- 39
- Issue:
- 5
- ISSN:
- 0263-5747
- Page Range / eLocation ID:
- 842 to 861
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1017/S0263574720000788
