Project Team
Students
Dalina Tu
Electrical Engineering
Penn State Harrisburg
Faculty Mentors
Seth Wolpert
Penn State Harrisburg
Electrical Engineering
Bo Cheng
Penn State University Park
Mechanical Engineering
Project
https://sites.psu.edu/mcreu/files/formidable/2/Dalinas-Official-MCREU.pdf
Project Video
Project 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.
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