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19 May 2020 Routing of an unmanned vehicle for classification
Christopher Montez, Swaroop Darbha, Christopher Valicka, Andrea Staid
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Proceedings Volume 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II; 1141319 (2020) https://doi.org/10.1117/12.2558748
Event: SPIE Defense + Commercial Sensing, 2020, Online Only
Abstract
Routing problems for unmanned vehicles are frequently encountered in civilian and military applications and have been studied extensively as a result. A routing problem of interest consists of constructing a tour that maximizes the total information gained over the course of the tour. Herein, we consider a version where information gain is represented by classification confidence at points of interest visited in the tour. The information gained at each point of interest is modeled using the Kullback-Leibler divergence (also referred to as mutual information) where the probability of correctly classifying the point of interest is taken to be time-dependent. A mixed-integer program (MIP) is formulated to model this problem and two standard heuristics (a modified two-step greedy algorithm and a standard 2-OPT algorithm) are combined in an attempt to produce high quality solutions. We run simulations with various conditions for the nature of the information gain and position of the points of interest. We show that combining these two heuristics produce near-optimal solutions in nearly all of the trials for up to 10 points of interest.
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Christopher Montez, Swaroop Darbha, Christopher Valicka, and Andrea Staid "Routing of an unmanned vehicle for classification", Proc. SPIE 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, 1141319 (19 May 2020); https://doi.org/10.1117/12.2558748
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KEYWORDS
Unmanned vehicles
Defense technologies
MATLAB
Scene classification
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