Informative soaring with drifting thermals

Abstract

The informative soaring (IFS) problem involves a gliding unmanned aerial vehicle (UAV) exploiting energy from thermals to extend its information gathering capability. In this paper, we address the realistic situation of detecting new thermals drifting with the wind in the search environment. We consider complex target-search scenarios characterised by information clusters and propose a new set of algorithms designed to both explore for and exploit high-value thermals to maximise information gain. Our algorithms: 1) compute a thermal exploration map to detect useful thermals that eventually intercept clusters, 2) solve a boundary value problem for interthermal path segment (ITP) generation with moving thermals, 3) compute thermal time windows to gather information from clusters and form a cluster service schedule, and 4) use branch and bound (BnB) tree search for global planning, considering high-utility-rate ITPs to maximise information gain. Our solution is compared against a greedy method that neither considers the thermal exploration map nor cluster schedule and a full knowledge method that has access to all thermals. Numerical simulations show that on average, our solution outperforms the greedy method in one-third of 2400 Monte Carlo trials, and achieves similar performance to the full knowledge method when environmental conditions are favourable.

Intelligent soaring and path planning for solar-powered unmanned aerial vehicles

“…Path planning is one of the most essential issues for UAVs to calculate an optimal trajectory between the starting point and destination (Aggarwal and Kumar, 2020). Different tree search methods have been developed to detect drift thermals to extend the information-gathering capability of the UAV (Nguyen et al, 2013(Nguyen et al, , 2016. Schermann et al (2019) studied an optimal manner soaring problem and achieved as many rounds as possible within a predetermined time (Schermann et al, 2019).…”