I. ABSTRACT We present a node placement algorithm for planning the deployment of a heterogeneous, underwater sensor network. Typical node placement algorithms do not account for heterogeneous node types and consequently, do not always provide accurate estimates for the total probability of success for the overall mission objective. In our approach, we derive an objective function that couples the probability of success for all node types to be used by a mixed-integer linear programming (MILP) solver for optimal placement. To reduce the computational intensity associated with the MILPbased approach, we provide an algorithm that converts the original optimization problem into several smaller optimization problems. We also describe the accompanying MILP framework that we have developed to create and maintain MILP problems.
Abstract-An important task in maritime search and inspection involves re-acquiring and identifying underwater objects by surveying the objects from multiple angles. Because of false contacts related to clutter on the sea floor, the objects are often detected in dramatically different densities in a given area. Previously developed methods to plan survey paths on groups of contacts led to efficient paths when the contacts occur in close proximity, but inefficient paths when the objects occur over large distances. We present a planning algorithm to generate an efficient path to survey objects from multiple angles that is independent of the density of the objects. The algorithm leverages the previously-developed algorithms for surveying objects from multiple directions, coupled with density-based spatial clustering of applications with noise (DBSCAN) clustering and ant colony optimization techniques.
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