Adaptive grid-based decomposition for UAV-based coverage path planning in maritime search and rescue

Unmanned aerial vehicles (UAVs) are increasingly utilized in search and rescue (SAR) operations to enhance efficiency by enabling rescue teams to cover large search areas in a shorter time. Reducing coverage time directly increases the likelihood of finding the target quickly, thereby improving the chances of a successful SAR operation. In this context, UAVs require path planning to determine the optimal flight path that fully covers the search area in the least amount of time. A common approach involves decomposing the search area into a grid, where the UAV must visit all cells to achieve complete coverage. In this paper, we propose an Adaptive Grid-based Decomposition (AGD) algorithm that efficiently partitions polygonal search areas into grids with fewer cells. Additionally, we utilize a Mixed-Integer Programming (MIP) model, compatible with the AGD algorithm, to determine a flight path that ensures complete cell coverage while minimizing overall coverage time. Experimental results highlight the efficiency of the AGD algorithm in reducing coverage time (by up to 20%) across various scenarios.

An exact coverage path planning algorithm for UAV-based search and rescue operations

Unmanned aerial vehicles (UAVs) are increasingly utilized in global search and rescue efforts, enhancing operational efficiency. In these missions, a coordinated swarm of UAVs is deployed to efficiently cover expansive areas by capturing and analyzing aerial imagery and footage. Rapid coverage is paramount in these scenarios, as swift discovery can mean the difference between life and death for those in peril. This paper focuses on optimizing flight path planning for multiple UAVs in windy conditions to efficiently cover rectangular search areas in minimal time. We address this challenge by dividing the search area into a grid network and formulating it as a mixed-integer program (MIP). Our research introduces a precise lower bound for the objective function and an exact algorithm capable of finding either the optimal solution or a near-optimal solution with a constant absolute gap to optimality. Notably, as the problem complexity increases, our solution exhibits a diminishing relative optimality gap while maintaining negligible computational costs compared to the MIP approach.