IEEE - Aerospace and Electronic Systems - February 2020 - 28

Coverage Mission for UAVs Using Differential Evolution and Fast Marching Square Methods

Figure 11.
Comparison of the resulting path against the ideal path and the terrain profile. (a) Path for S1. (b) Path for S2. (c) Path for S3. (d) Path for S4.

of smoothness and safety with a minimum distance cost,
maintaining a fixed flight level with respect to the ground
and avoiding any obstacle in the terrain. The cost of the
path corresponds with its length.
First, the Zigzag path method is used for the generation of the zigzag bands, and then the DE algorithm optimizes them so that the steering angle is optimal, ensuring
a minimal distance cost. Later, the map W0 is modified
according to the obtained zigzag path, and the FM2
method is applied over this map. Two adjustment parameters p1 and p2 have been used to adjust the path to the
kinematics of the UAV and to force the path planning in
specific areas of the 3-D map determined by a given flight
level. The generated path is optimal not only in terms of
the distance cost, but also in terms of safety and smoothness, and therefore, feasible for the UAV.
Our approach has been proven with successful results.
The approach works in different irregular surfaces and for
different vision fields of the UAV, obtaining always the
feasible path with the minimum cost. This results demonstrate that our approach generates paths to save energy or
fuel, and in addition generates trajectories compatibles
with the kinematics of the UAV.
In a further research, the explicit relationship
between the adjustment parameters p1 and p2 and the
kinematic and dynamic constraints of the UAV will be
studied.

REFERENCES
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ACKNOWLEDGMENT

ocean structures," Appl. Soft Comput., vol. 24, pp. 95-108,

This work was supported by the RoboCity2030-III-CM
project (Robotica 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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2014.
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IEEE A&E SYSTEMS MAGAZINE

ferential evolution algorithm for unmanned aerial vehicle
global route planning," Appl. Soft Comput., vol. 26,
pp. 270-284, 2015.

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