Coverage Path Planning (CPP) is a well-known problem which objective is to find an optimal plan for a fleet of UAVs covering a certain area of interest in the shortest overall time. CPP has a practical impact even in its static formulation, due to the growing interest in UAVs and their numerous applications such as surveillance, terrain coverage, mapping, natural disaster tracking, transport, and others. Unfortunately, CPP is NP-hard, so that it is computationally infeasible to obtain an exact solution for a large number of UAVs and regions. Different approaches are available in the literature to face the above problem. In this paper, we propose a novel approach working in the case of autonomous heterogeneous UAVs, based on a greedy technique. The method appears promising for its nice features of computational efficiency and potential adaptivity in the case of dynamic settings.
A Greedy Method for Coverage Path Planning of Autonomous Heterogeneous UAVs.
Buccafurri Francesco
;
2023-01-01
Abstract
Coverage Path Planning (CPP) is a well-known problem which objective is to find an optimal plan for a fleet of UAVs covering a certain area of interest in the shortest overall time. CPP has a practical impact even in its static formulation, due to the growing interest in UAVs and their numerous applications such as surveillance, terrain coverage, mapping, natural disaster tracking, transport, and others. Unfortunately, CPP is NP-hard, so that it is computationally infeasible to obtain an exact solution for a large number of UAVs and regions. Different approaches are available in the literature to face the above problem. In this paper, we propose a novel approach working in the case of autonomous heterogeneous UAVs, based on a greedy technique. The method appears promising for its nice features of computational efficiency and potential adaptivity in the case of dynamic settings.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
