Optimal mission path planning (MPP) for an air sampling unmanned aerial system

Abstract

This paper presents advanced optimization
techniques for Mission Path Planning (MPP) of a
UAS fitted with a spore trap to detect and
monitor spores and plant pathogens. The UAV
MPP aims to optimise the mission path planning
search and monitoring of spores and plant
pathogens that may allow the agricultural sector
to be more competitive and more reliable. The
UAV will be fitted with an air sampling or spore
trap to detect and monitor spores and plant
pathogens in remote areas not accessible to
current stationary monitor methods.
The optimal paths are computed using a
Multi-Objective Evolutionary Algorithms
(MOEAs). Two types of multi-objective
optimisers are compared; the MOEA
Non-dominated Sorting Genetic Algorithms II
(NSGA-II) and Hybrid Game are implemented to
produce a set of optimal collision-free
trajectories in three-dimensional environment.
The trajectories on a three-dimension terrain,
which are generated off-line, are collision-free
and are represented by using Bézier spline curves
from start position to target and then target to
start position or different position with altitude
constraints. The efficiency of the two
optimization methods is compared in terms of
computational cost and design quality.
Numerical results show the benefits of coupling
a Hybrid-Game strategy to a MOEA for MPP
tasks. The reduction of numerical cost is an
important point as the faster the algorithm
converges the better the algorithms is for an
off-line design and for future on-line decisions of
the UAV.