Browsing by Author "Yetkin, Harun"

Now showing 1 - 3 of 3
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    Environmental Information Improves Robotic Search Performance
    Yetkin, Harun; Lutz, Collin C.; Stilwell, Daniel J. (Virginia Tech, 2016)
    We address the problem where a mobile search agent seeks to find an unknown number of stationary objects distributed in a bounded search domain, and the search mission is subject to time/distance constraint. Our work accounts for false positives, false negatives and environmental uncertainty. We consider the case that the performance of a search sensor is dependent on the environment (e.g., clutter density), and therefore sensor performance is better in some locations than in others. For applications where environmental information can be acquired, we derive a decision-theoretic cost function to compute the locations where the environmental information should be acquired. We address the cases where environmental characterization is performed either by a separate vehicle or by the same vehicle that performs the search task.
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    On the Role of Uncertainty in Poisson Target Models Used for Placement of Spatial Sensors
    Kim, Mingyu; Yetkin, Harun; Stilwell, Daniel J.; Jimenez, Jorge (SPIE, 2023-01-01)
    This paper addresses the role of uncertainty in spatial point-process models, such as those that might arise in modelling ship traffic. We consider a doubly stochastic Poisson point process where the intensity function is uncertain. To assess the role of uncertainty, we conduct a large set of numerical trials where we estimate a doubly stochastic Poisson point-process model from historical target data, and the evaluate the model by assessing the target detection performance of a set of sensors whose locations are selected using the model. Our work is motivated by seabed sensors that detect ship traffic, and we conduct numerical trials using historical ship traffic data near the mouth of the Chesapeake Bay, Virginia, USA, that was recorded by the Automated Identification System.
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    Toward Real-Time Planning for Robotic Search
    Yetkin, Harun (Virginia Tech, 2018-09-12)
    This work addresses applications of search theory where a mobile search agent seeks to find an unknown number of stationary targets randomly distributed in a bounded search domain. We assume that the search mission is subject to a time or distance constraint, and that the local environmental conditions affect sensor performance. Because the environment varies by location, the effectiveness of the search sensor also varies by location. Our contribution to search theory includes new decision-theoretic approaches for generating optimal search plans in the presence of false alarms and uncertain environmental variability. We also formally define the value of environmental information for improving the effectiveness of a search mission, and we develop methods for optimal deployment of assets that can acquire environmental information in order to improve search effectiveness. Finally, we extend our research to the case of multiple cooperating search agents. For the case that inter-agent communication is severely bandwidth-limited, such as in subsea applications, we propose a method for assessing the expected value of inter-agent communication relative to joint search effectiveness. Our results lead to a method for determining when search agents should communicate. Our contributions to search theory address important applications that range from subsea mine-hunting to post-disaster search and rescue applications.