Publications
Dragonfly-Inspired Algorithms for Intercept Trajectory Planning
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.