Real-time Monitoring and Estimation of Spatio-Temporal Processes Using Co-operative Multi-Agent Systems for Improved Situational Awareness
Balaji R. Sharma · OhioLink ETD Center (Ohio Library and Information Network) · 2013
This work is intended towards the development of a framework for the deployment of a distributed multi-agent system, such as a fleet of unmanned aerial vehicles (UAVs), 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 for the monitoring of spatio-temporal processes has strong advantages over conventional methods such as satellite imagery, and presents fewer risks than manned aerial missions.Development of such a framework around autonomous, unmanned multi-vehicle systems 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.Of critical importance in such strategies is the need for agents to converge to stable spatially well-distributed pursuit configurations based purely on local inter-agent interactions.Also critical is their robustness to dynamic addition and deletion of agents without adversely affecting system stability.To this effect, a linear cyclic pursuit control model based on double integrator dynamics has been developed, and the convergence of a system of 3.31 Convergence of agent velocities (magnitude) . . . . . . . . . .4.1 Initial fire location (T (in K)) . . . . . . . . . . . . . . . . . .4.2 Errors relative to number of modes used for POD-based reconstruction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4.3 Temporal comparison of estimates with process and measurement data at gridpoint (29,2). . . . . . . . . . . . . . . . . . .4.4 Spatial comparison of estimates with process and measurement data at the 10th timestep (K). . . . . . . . . . . . . . . . . . .4.5 Temporal comparison of estimates with process and measurement data at gridpoint (21,12). . . . . . . . . . . . . . . . . .4.6 Spatial comparison of estimates with process and measurement data at the 40th timestep (in (K). . . . . . . . . . . . . . . . .4.7 Comparison of RMS errors with respect to actual process states: (L) Filtered estimates (R) Measurement data. . . . . . . . . .