Mission planning for emergency rapid mapping with drones

TL;DR

This paper presents a novel mission planning approach for UAVs to efficiently survey hazardous areas post-incident, using spatially-aware sampling and an advanced optimization algorithm to improve rapid mapping accuracy.

Contribution

It introduces the generalized correlated team orienteering problem (GCorTOP) and a two-phase multi-start adaptive large neighborhood search (2MLS) for effective UAV routing in emergency mapping.

Findings

The proposed method outperforms existing benchmarks in solution quality.

GCorTOP effectively models spatial correlations in sampling.

The approach provides rapid, high-quality solutions suitable for time-sensitive scenarios.

Abstract

We introduce a mission planning concept for routing unmanned aerial vehicles (UAVs) through a set of sampling locations in the immediate aftermath of an incident such as a fire or chemical accident. Using interpolation methods that account for the spatial interdependencies inherent in the surveyed phenomenon, these samples allow predicting the distribution of hazardous substances across the affected area. We define the generalized correlated team orienteering problem (GCorTOP) for selecting {informative} samples considering spatial correlations between observed and unobserved locations as well as priorities in the surveyed area. To quickly provide high-quality solutions in time-sensitive situations, we propose a two-phase multi-start adaptive large neighborhood search (2MLS). We show the competitiveness of the solution approach using benchmark instances for the team orienteering problem and investigate the performance of the proposed models and solution approach in an extensive study based on newly introduced benchmark instances for the mission planning problem.