Monitoring of biological and chemical pollutants in large
bodies of water requires the acquisition of a large number
of in-situ measurements by a mobile sensor platform.
Critical to this problem is an efficient path planning method,
easily adaptable to different control strategies that
ensure the collection of data of the greatest value. This
paper proposes a deliberative path planning algorithm,
which features the use of waypoints for a ship navigation
trajectory that are generated by Genetic Algorithm (GA)
based procedures. The global search abilities of Genetic
Algorithms are combined with the heuristic local search
in order to implement a navigation behaviour suitable
to the required data collection strategy. The adaptive
search system operates on multi-layer maps generated
from remote sensing data, and provides the capacity
for dealing with multiple classes of water pollutants. A
suitable objective function was proposed to handle different
sampling strategies for the collection of samples
from multiple water pollutant classes. A region-of-interest
(ROI) component was introduced to deal effectively
with the large scale of search environments by pushing
the search towards ROI zones. This resulted in the reduction
of the search time and the computing cost, as well as
good convergence to an optimal solution. The global path
planning performance was further improved by multipoint
crossover operators running in each GA generation.
The system was developed and tested for inland water
monitoring and trajectory planning of a mobile sample
acquisition platform using commercially available satellite
data.
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