RT Journal Article
T1 Coverage mission for UAVs using differential evolution and fast marching square methods
A1 González Pérez, Verónica
A1 Monje Micharet, Concepción Alicia
A1 Garrido Bullón, Luis Santiago
A1 Moreno Lorente, Luis Enrique
A1 Balaguer Bernaldo de Quirós, Carlos
AB This research presents a novel approach for missions of coverage path planning (CPP) carried out by unmanned aerial vehicles (UAVs) in a three-dimensional environment. These missions are focused on path planning to cover a certain area in an environment in order to carry out tracking, search, or rescue tasks. The methodology followed uses an optimization process based on the differential evolution (DE) algorithm in combination with the Fast Marching Square (FM2) planner. The DE algorithm evaluates a cost function to determine what the zigzag path with the minimum cost is, according to the steering angle of the zigzag bands (alfa). This optimization process allows achieving the most optimal zigzag path in terms of distance traveled by the UAV to cover the whole area. Then, the FM2 method is applied to generate the final path according to the steering angle of the zigzag bands resulting from the DE algorithm. The approach generates a feasible path free from obstacles, keeping a fixed altitude flight over the ground. The flight level, smoothness, and safety of the path can be modified by two adjustment parameters included in our approach. Simulated experiments carried out in this work demonstrate that the proposed approach generates the most optimal zigzag path in terms of distance, safety, and smoothness to cover a certain whole area, keeping a determined flight level with successful results.
PB IEEE
SN 0885-8985
YR 2020
FD 2020-02-01
LK https://hdl.handle.net/10016/33911
UL https://hdl.handle.net/10016/33911
LA eng
NO This work was supported by the RoboCity2030-III-CM project (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos, fase III; S2013/MIT-2748), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU.
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RD 12 sept. 2024