Autonomous Light-Seeking Robotic System Inspired by Dynamic Insect Vision

For a better view of my poster while you watch, open this link: https://sites.psu.edu/mcreu2020/files/formidable/2/Dalinas-Official-MCREU.pdf

To return to my MCREU project page and submit an evaluation for my project: https://sites.psu.edu/mcreu2020/2020/07/28/autonomous-light-seeking-robotic-system-inspired-by-dynamic-insect-vision/

Be sure to turn up your volume!

Dalina Thuy Mi Tu, 

Campus: Harrisburg, University Park, 

Anticipated Graduation: May 2022, 

Mentors: Seth Wolpert (Harrisburg), Bo Cheng (University Park), 

Project Title: Autonomous Light-Seeking Robotic System Inspired by Dynamic Insect Vision

ABSTRACT-

The project constructed an autonomous, mobile, light-seeking robot with a vision method inspired by insects. The robot had to be capable of adapting in unstructured environments, differentiating light intensities, steering to the highest luminance areas, and continuously following an erratically moving light source. These abilities were each measured through various trials of courses. Light tracking robotics proves useful to applications like farming, solar light tracking, and firefighting systems. Vehicles that detect and track motion have applications like surveillance and search and rescue. Vision is an essential sense for robots to understand and interact with the world. Emulating insects could help advance robots, which help people globally: elevating manufacturers, pioneering space exploration, saving lives, and cleaning family homes. Humans have the spatial perception to effortlessly analyze environments, things, and motion, but forming such aptitudes in robots remains a work-in-progress. A literature study was performed on certain insects owning some visual acuities that are superior to humans, although having smaller, simpler anatomy. Interest was in the anatomy and processing they own to see three-dimensionally and quickly. A comparison between current visual technology and the structural organization of the photoreceptors of adult insects was performed. The learned information was to be inspirational. At the end of the project, the robot completed all its planned tasks. Also, plentiful knowledge was learned in electrical engineering. However, with the constraint of time and resources, satisfactory inclusion of the insect principles was not achieved. In the next steps, that will be the priority. Also, the robot will acquire more efficient and powerful mechanical and electronic parts. Further in the future, the capacity for deep learning AI may be added, to dive into the neural network of insects. This approach supports the application of biological mechanics to advance technology and supports robotic vision itself.

Acknowledgments

This research was funded by the Penn State Multi-Campus Research Experience for Undergraduates (MC REU) program. I would like to thank Penn State, Erin Hostetler and Cindy Reed for this opportunity. I am grateful to Dr. Seth Wolpert and Dr. Bo Cheng for their mentorship, flexibility, patience, and wisdom.