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Information-Driven Path Planning for UAV with Limited Autonomy in Large-scale Field Monitoring

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Document pages: 11 pages

Abstract: This paper presents a novel information-based mission planner for a dronetasked to monitor a spatially distributed dynamical phenomenon. For the sake ofsimplicity, the area to be monitored is discretized. The insight behind theproposed approach is that, thanks to the spatio-temporal dependencies of theobserved phenomenon, one does not need to collect data on the entire area. Infact, unmeasured states can be estimated using an estimator, such as a Kalmanfilter. In this context the planning problem becomes the one of generating aflight path that maximizes the quality of the state estimation while satisfyingthe flight constraints (e.g. flight time). The first result of this paper is toformulate this problem as a special Orienteering Problem where the costfunction is a measure of the quality of the estimation. This approach providesa Mixed-Integer Semi-Definite formulation to the problem which can be optimallysolved for small instances. For larger instances, two heuristics are proposedwhich provide good sub-optimal results. To conclude, numerical simulations areshown to prove the capabilities and efficiency of the proposed path planningstrategy. We believe this approach has the potential to increase dramaticallythe area that a drone can monitor, thus increasing the number of applicationswhere monitoring with drones can become economically convenient.

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