Probabilistic Search Optimization and Mission Assignment for Heterogeneous Autonomous Agents
Chung, Timothy H.
Royset, Johannes O.
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This paper presents an algorithmic framework for conducting search and identification missions using multiple heterogeneous agents. Dynamic objects of type "neutral" or "target" move through a diecretized environment. Probabilistic representation of the current level of situational awareness - knowledge of belief of object locations and identities - is updated with imperfect observations. Optimization of search is formulated as a mixed-integer program to maximize the expected number of targets found and solved efficiently in a receding horion approach. The search effeort is conducted in tandem with object identification and target interception tasks, and a method for assignment of these missions is demonstrated in simulation studies, and an implementation of its decision support capabilities in a recent field experiment is reported.
2009 IEEE International Conference on Robotics and Automation, Kobe, Japan. 2009.
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