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Title: Dragonfly-Inspired Algorithms for Intercept Trajectory Planning.

Abstract

Dragonflies are known to be highly successful hunters (achieving 90 - 95% success rate in nature) that implement a guidance law like proportional navigation to intercept their prey. This project tested the hypothesis that dragonflies are able to implement p roportional navigation using prey - image translation on their eyes. The model dragonfly presented here calculates changes in pitch and yaw to maintain the prey's image at a designated location (the fovea) on a two - dimensional screen (the model's eyes ). Wh en the model also uses self - knowledge of its own maneuvers as an error signal to adjust the location of the fovea, its interception trajectory becomes equivalent to proportional navigation. I also show that this model can also be applied successfully (in a liminted nu mber of scenarios) against maneuvering prey. My results provide a proof - of - concept demonstration of the potential of using the dragonfly nervous system to design a robust interception algorithm for implementation on a man - made system. ACKNOWLEDGEME NTS First I would like to thank the Autonomy for Hypersonics Mission Campaign for their support of this LDRD. I am also grateful to Larry Jones, Julie Parish, Jeff Spooner, Bradmore » Aimone, Fred Rothganger and Srideep Musuvathy for helpful discussions during development of this model.« less

Authors:
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1569338
Report Number(s):
SAND2019-11695
679760
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English

Citation Formats

Chance, Frances S. Dragonfly-Inspired Algorithms for Intercept Trajectory Planning.. United States: N. p., 2019. Web. doi:10.2172/1569338.
Chance, Frances S. Dragonfly-Inspired Algorithms for Intercept Trajectory Planning.. United States. doi:10.2172/1569338.
Chance, Frances S. Sun . "Dragonfly-Inspired Algorithms for Intercept Trajectory Planning.". United States. doi:10.2172/1569338. https://www.osti.gov/servlets/purl/1569338.
@article{osti_1569338,
title = {Dragonfly-Inspired Algorithms for Intercept Trajectory Planning.},
author = {Chance, Frances S.},
abstractNote = {Dragonflies are known to be highly successful hunters (achieving 90 - 95% success rate in nature) that implement a guidance law like proportional navigation to intercept their prey. This project tested the hypothesis that dragonflies are able to implement p roportional navigation using prey - image translation on their eyes. The model dragonfly presented here calculates changes in pitch and yaw to maintain the prey's image at a designated location (the fovea) on a two - dimensional screen (the model's eyes ). Wh en the model also uses self - knowledge of its own maneuvers as an error signal to adjust the location of the fovea, its interception trajectory becomes equivalent to proportional navigation. I also show that this model can also be applied successfully (in a liminted nu mber of scenarios) against maneuvering prey. My results provide a proof - of - concept demonstration of the potential of using the dragonfly nervous system to design a robust interception algorithm for implementation on a man - made system. ACKNOWLEDGEME NTS First I would like to thank the Autonomy for Hypersonics Mission Campaign for their support of this LDRD. I am also grateful to Larry Jones, Julie Parish, Jeff Spooner, Brad Aimone, Fred Rothganger and Srideep Musuvathy for helpful discussions during development of this model.},
doi = {10.2172/1569338},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {9}
}