Discrete search allocation with object uncertainty

Wettergren, Thomas A.; Baylog, John G.

doi:10.1504/ijor.2014.060513

Cited by 5 publications

(1 citation statement)

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“…In this case, the outer algorithm solves the simple scheduling problem of finding the best time sequence under the assumption that the selection of best modes for a fixed time sequence is readily available. To obtain the modes for any fixed time sequence (the inner algorithm), we utilize a greedy sort algorithm, which has been previously shown 18 to be optimal in multi-stage search planning problems.…”

Section: Inner/outer Optimization Solution Techniquementioning

confidence: 99%

Optimal vehicle planning and the search tour problem

Wettergren

Bays

2016

SPIE Proceedings

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We describe a problem of optimal planning for unmanned vehicles and illustrate two distinct procedures for its solution. The problem under consideration, which we refer to as the search tour problem, involves the determination of multi-stage plans for unmanned vehicles conducting search operations. These types of problems are important in situations where the searcher has varying performance in different regions throughout the domain due to environmental complexity. The ability to provide robust planning for unmanned systems under difficult environmental conditions is critical for their use in search operations. The problem we consider consists of searches with variable times for each of the stages, as well as an additional degree of freedom for each stage to select from one of a finite set of operational configurations. As each combination of configuration and stage time leads to a different performance level, there is a need to determine the optimal configuration of these stages. When the complexity of constraints on total time, as well as resources expended at each stage for a given configuration, are added, the problem becomes one of non-trivial search effort allocation and numerical methods of optimization are required. We show two solution approaches for this numerical optimization problem. The first solution technique is to use a mixed-integer linear programming formulation, for which commercially available solvers can find optimal solutions in a reasonable amount of time. We use this solution as a baseline and compare against a new inner/outer optimization formulation. This inner/outer optimization compares favorably to the baseline solution, but is also amenable to adaptation as the search operation progresses. Numerical examples illustrate the utility of the approach for unmanned vehicle search planning.

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Section: Inner/outer Optimization Solution Techniquementioning

confidence: 99%