Abstract
In the event of a maritime accident, surveying the maximum area efficiently in the least amount of time is crucial for rescuing survivors. Increasingly, unmanned aerial vehicles (UAVs) are being used in search and rescue operations. This study proposes a method to generate a search path that covers all generated nodes in the shortest amount of time with multiple heterogeneous UAVs. The proposed model, which is a mixed-integer linear programming (MILP) model based on a hexagonal grid-based decomposition method, was verified through a simulation analysis based on the performance of an actual UAV. This study presents both the optimization technique’s calculation time as a function of the search area size and the various UAV routes derived as the search area grows. The results of this study can have wide-ranging applications for emergency search and rescue operations.
Highlights
In Korea, an average of 2631 marine accidents have occurred per year over the last six years (Figure 1)
Marine accidents in Korea result in casualties and ongoing environmental and property damage
Multiple heterogeneous Unmanned aerial vehicles (UAVs) were utilized to search for casualties in a short period of time to protect lives from catastrophic marine accidents and to prevent the spread of damage
Summary
In Korea, an average of 2631 marine accidents have occurred per year over the last six years (Figure 1). The grid-based decomposition method divides the region without considering the region’s characteristics or the UAV, making it simple to divide the region but increasing the complexity of planning the coverage path. This study focuses on generating the optimal search path for multiple UAVs by segmenting the target area using the grid-based decomposition method. This study proposes a hexagonal grid-based decomposition method and a mixed-integer linear programming (MILP) model to generate a search path that covers all generated nodes in the shortest amount of time with multiple heterogeneous UAVs. The contributions of our study are twofold as follows. CPP problem decomposed the target area into subregions and hierarchically interpreted the problem as a multiple CPP problem of a single UAV for each subregion This method can generate an inefficient path because it cannot plan a path that entirely considers the performance of heterogeneous UAVs. The remainder of this paper is organized as follows.
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