Development of a Model and Estimation Method to Represent Team Search with Uncertain Detection

Audrey Balaska and Jason H. Rife

Abstract: This paper introduces an approach for modeling a search task conducted by heterogeneous robot teams or mixed human/robot teams. The novelty of our model is that the process of object finding is represented as a probabilistic detection (or monitoring) process. This means searchers may need to revisit areas of a search domain more than once, since they are not guaranteed to find objects even when those objects are nearby. Based on our model, an estimation method is introduced to infer the skill of the searchers and the number of missing objects, both of which are assumed unknown. Proof-of-concept simulations were run to demonstrate the utility of the estimator, including one simulation involving a single searcher (or agent) and another simulation involving two agents. In our simulations, both the single-agent and two-agent cases were successful in predicting the number of hidden objects not yet found.
Published in: Proceedings of the 2020 International Technical Meeting of The Institute of Navigation
January 21 - 24, 2020
Hyatt Regency Mission Bay
San Diego, California
Pages: 636 - 650
Cite this article: Balaska, Audrey, Rife, Jason H., "Development of a Model and Estimation Method to Represent Team Search with Uncertain Detection," Proceedings of the 2020 International Technical Meeting of The Institute of Navigation, San Diego, California, January 2020, pp. 636-650.
https://doi.org/10.33012/2020.17167
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