Ant colony optimization for data acquisition mission planning

The probabilistic Ant Colony Optimization (ACO) approach is presented to solve the problem of designing an optimal trajectory for a mobile data acquisition platform. An ACO algorithm optimizes an objective function defined in terms of the value of the acquired data samples subject to different sets of constraints depending on the current data acquisition strategy. The analysis presented in this paper focuses on an environment monitoring system, which acquires in-situ data for precise calibration of a water quality monitoring system. The value of the sample is determined based on the concentration of the water pollutant, which in turn is obtained through processing of multi-spectral satellite imagery. Since our problem is defined in a continuous space of coordinates, and in some strategies each point is able to connect to any other point in the space, we adopted a hybrid model that involves a connection graph and also a spatial grid.