Drone Searches in Challenging Conditions

Abstract

The planning of drone trajectories in the search for ground-level objects is considered, in the case of challenging observation conditions. In existing planning algorithms, a priori data regarding the location of the objects are used in the secondary search, but no account is taken of their local visibility (on account of fog or smoke, say). To improve search productivity in challenging observation conditions, the observability may be taken into account in planning the drone trajectory. Heuristic models are used to assess the observability. The working trajectory is selected by calculating the maximum useful search information obtained from various possible trajectories. To eliminate Shannon indeterminacy in the utility of the information regarding successive points, we introduce an additional utility function. The results obtained by simulation of the search process confirm that this approach is more effective than trajectory planning on the basis of the maximum a priori probability that objects are present and on the basis of search entropy estimates.