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

TL;DR

This paper introduces an information-based path planning method for UAVs that maximizes state estimation quality in large-scale field monitoring by formulating it as an Orienteering Problem with heuristics for large instances.

Contribution

It formulates UAV path planning as an estimation quality maximization problem using a novel Mixed-Integer Semi-Definite programming approach and heuristics for scalability.

Findings

Optimally solves small instances of the problem.

Provides effective heuristics for large-scale scenarios.

Numerical simulations demonstrate improved monitoring efficiency.

Abstract

This paper presents a novel information-based mission planner for a drone tasked to monitor a spatially distributed dynamical phenomenon. For the sake of simplicity, the area to be monitored is discretized. The insight behind the proposed approach is that, thanks to the spatio-temporal dependencies of the observed phenomenon, one does not need to collect data on the entire area. In fact, unmeasured states can be estimated using an estimator, such as a Kalman filter. In this context the planning problem becomes the one of generating a flight path that maximizes the quality of the state estimation while satisfying the flight constraints (e.g. flight time). The first result of this paper is to formulate this problem as a special Orienteering Problem where the cost function is a measure of the quality of the estimation. This approach provides a Mixed-Integer Semi-Definite formulation to the problem which can be optimally solved for small instances. For larger instances, two heuristics are proposed which provide good sub-optimal results. To conclude, numerical simulations are shown to prove the capabilities and efficiency of the proposed path planning strategy. We believe this approach has the potential to increase dramatically the area that a drone can monitor, thus increasing the number of applications where monitoring with drones can become economically convenient.