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Real-time Monitoring and Estimation of Spatio-Temporal Processes Using Co-operative Multi-Agent Systems for Improved Situational Awareness

Sharma, Balaji R.
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1382950922

Abstract Details

2013, PhD, University of Cincinnati, Engineering and Applied Science: Mechanical Engineering.
This work is intended towards the development of a framework for the deployment of a distributed multi-agent system for co-operative monitoring of spatio-temporal processes in applications such as wildland fires and utilizing the information thus obtained for improved situational awareness. The use of such co-operative systems has strong advantages over conventional methods and presents fewer risks than manned aerial missions. Development of such a framework requires addressing several challenges in sensing, control, optimization, estimation and related technologies. Towards such a framework, this dissertation work focuses on two significant aspects of its development: (i) cooperative control in a multi-agent system for distributed data gathering, and (ii) development of a data processing and filtering algorithm for spatio-temporal estimation. To achieve the first objective, this work develops a co-operative control strategy that optimizes the spatial distribution of agents around closed curves that typically represent most dynamic perimeters. A linear cyclic pursuit control model based on double integrator dynamics has been developed, and the convergence of a system of agents governed by this control model to a stable well-distributed pursuit configuration is demonstrated. The theories developed around the co-operative control model and pursuit dynamics are validated over real-time experiments involving a group of ground robots under the influence of the controller. Further, the influence of addition of a non-linear repulsive term to this control model and its influence on the stability of the control model is evaluated numerically. The control model, further, is extended towards tracking general forms of closed towards practical implementation in real-world applications. A two-dimensional controller is developed in this regard, where the aforementioned cyclic pursuit control action acting along a closed curve is augmented with an orthogonal radial perimeter tracking component, resulting in concurrent perimeter tracking and cyclic pursuit. The latter stage of this work is focused towards real-time estimation of dynamic spatio-temporal processes for improved situational awareness. The objective is to fuse mathematical prediction models with real-time field measurements using a spatio-temporal Kalman filtering technique. Conventional Bayesian estimation methods are high-dimensional techniques that are computationally intensive for large space-time problems, and do not lend themselves very well for real-time applications, and therefore, a space-time Kalman filter, implemented in conjunction with concepts in Proper Orthogonal Decomposition (POD), is presented towards dimensionality reduction in the estimation process. A coupled PDE model for fire growth used in conjunction with a multivariate autoregressive model estimation forms the basis for state prediction, and noisy measurements of wildfire spread from an agent within its field of view provide the necessary measurement data. The predicted and measured states form the basis for improved state estimation of wildfire spread, thus enabling a greater level of situational awareness regarding the wildland fire. The techniques presented in this work thus act as enablers for a broader objective of creating a framework for the use of unmanned multi-agent systems for monitoring spatio-temporal events such as wildland fires, with potential benefits in improved prediction capabilities towards better resource management.
Manish Kumar, Ph.D. (Committee Chair)
Randall Allemang, Ph.D. (Committee Member)
Kelly Cohen, Ph.D. (Committee Member)
David Thompson, Ph.D. (Committee Member)
137 p.
Mechanics
Multi-agent systems; Cooperative Control; Spatio-temporal Estimation; Unmanned Aerial Vehicles

Recommended Citations

Citations

  • Sharma, B. R. (2013). Real-time Monitoring and Estimation of Spatio-Temporal Processes Using Co-operative Multi-Agent Systems for Improved Situational Awareness [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1382950922

    APA Style (7th edition)

  • Sharma, Balaji. Real-time Monitoring and Estimation of Spatio-Temporal Processes Using Co-operative Multi-Agent Systems for Improved Situational Awareness. 2013. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1382950922.

    MLA Style (8th edition)

  • Sharma, Balaji. "Real-time Monitoring and Estimation of Spatio-Temporal Processes Using Co-operative Multi-Agent Systems for Improved Situational Awareness." Doctoral dissertation, University of Cincinnati, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1382950922

    Chicago Manual of Style (17th edition)

ucin1382950922
507
© 2013, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.