Decentralized Data-Driven Control of Cooperating Sensor-Carrying UAVs in a Multi-Objective Monitoring Scenario

Abstract For estimating atmospheric dispersion of harmful material, the use of multiple sensor-equipped UAVs for information gathering offers great flexibility, but requires an efficient adaptive sampling strategy that exploits multi-vehicle cooperation. For this purpose, a novel decentralized data-driven online control scheme for cooperating vehicles in multi-objective monitoring scenarios is presented in this paper. In the considered use case, multiple UAVs are to adaptively gather measurements for estimating the parameters of an atmospheric dispersion model. At the same time, they are required to cooperatively patrol predefined checkpoints. Vehicle-specific optimal waypoints for each UAV are determined by sequential optimum design. Following these waypoints leads to a maximized information gain of the acquired measurements, such that the parameter estimate is iteratively improved. On the other hand, checkpoint allocation as well as trajectory planning is provided by a decentralized model-predictive controller based on a discrete-time mixed-integer linear problem formulation. By permanent interaction of parameter estimation, waypoint calculation, and cooperative control, a fully optimization-based, yet efficient and adaptive feedback control approach is obtained. Simulations successfully demonstrate its effectiveness.

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