Multi-UAV Rapid-Assessment Task-Assignment Problem in a Post-Earthquake Scenario

Abstract

The rapid assessment of earthquake-stricken regions immediately after a seismic event is crucial for earthquake relief operations. Since unmanned aerial vehicles (UAVs) can quickly reach the affected areas and obtain images, they are widely used in the post-earthquake rapid assessment. However, sensor noise and other unavoidable errors can affect the quality of images acquired by sensors attached to the UAVs, which can, in turn, reduce the quality of the assessment. We defined a new problem in the application of multiple UAVs in the rapid assessment of earthquake-stricken regions. The rapid-assessment task-assignment problem (RATAP) was used to construct the assignment plan for multiple UAVs in a rapid-assessment task while considering the weights of potential targets, the endurance of the UAVs, and the sensor errors. The RATAP was formulated as a variant of the team orienteering problem (TOP) called the revisit-allowed TOP with reward probability (RTOP-RP). We then developed an efficient hybrid particle swarm optimization with simulated annealing (HPSO-SA) algorithm, which produced a high-quality solution for the RATAP, and confirmed the effectiveness and rapidity of our algorithm through numerical experiments. Finally, we conducted a case study based on real-world data from the 2008 Wenchuan earthquake in China to demonstrate our approach.