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

TL;DR

This paper presents an exact algorithm for UAV path planning in search and rescue missions, optimizing coverage time in windy conditions by solving a mixed-integer program with proven bounds.

Contribution

It introduces a novel exact algorithm with a tight lower bound for multi-UAV coverage path planning under wind disturbances, improving solution accuracy and efficiency.

Findings

The algorithm finds optimal or near-optimal paths with minimal computational cost.

The method maintains a small optimality gap even as problem complexity increases.

It effectively handles windy conditions in search area coverage.

Abstract

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.