Automated modeling of the guidance of a K-9. Britt, W., Bevly, D. M., & Dozier, G. In 2008 American Control Conference, pages 2467–2474, June, 2008. ISSN: 2378-5861![Automated modeling of the guidance of a K-9 [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex This paper attempts to automate and replace human guidance in the control of a K-9 unit by modeling that guidance from observation. The ultimate research goal seeks to contribute toward the autonomous command of a trained K-9 unit by analyzing the movement and the behavior of the dog as it responds to command tones. Specifically, GPS and command signal information (from a human trainer) is recorded as a canine follows (or fails to follow) instructions as it moves toward a destination. The data is then processed into training instances and used as training data for a general regression neural network (GRNN). Then, the network is used to classify previously unseen test instances to determine if the behavior at that moment is normal or anomalous (in need of correcting tones). Both representation of training instances and the system parameters of the GRNN are optimized using a simple evolutionary hill-climber (EHC). Given even fairly limited initial data for training, the system performs well, producing relatively few false positives and false negatives in classification.
@inproceedings{britt_automated_2008,
title = {Automated modeling of the guidance of a {K}-9},
url = {https://ieeexplore.ieee.org/document/4586861/;jsessionid=82D5EBD9912D34052FDAA7827283D4D0},
doi = {10.1109/ACC.2008.4586861},
abstract = {This paper attempts to automate and replace human guidance in the control of a K-9 unit by modeling that guidance from observation. The ultimate research goal seeks to contribute toward the autonomous command of a trained K-9 unit by analyzing the movement and the behavior of the dog as it responds to command tones. Specifically, GPS and command signal information (from a human trainer) is recorded as a canine follows (or fails to follow) instructions as it moves toward a destination. The data is then processed into training instances and used as training data for a general regression neural network (GRNN). Then, the network is used to classify previously unseen test instances to determine if the behavior at that moment is normal or anomalous (in need of correcting tones). Both representation of training instances and the system parameters of the GRNN are optimized using a simple evolutionary hill-climber (EHC). Given even fairly limited initial data for training, the system performs well, producing relatively few false positives and false negatives in classification.},
urldate = {2024-06-20},
booktitle = {2008 {American} {Control} {Conference}},
author = {Britt, Winard and Bevly, David M. and Dozier, Gerry},
month = jun,
year = {2008},
note = {ISSN: 2378-5861},
keywords = {Automatic control, Computer science, Data security, Global Positioning System, Humans, Intelligent robots, Intelligent sensors, Neural networks, Testing, Training data},
pages = {2467--2474},
}
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