Sub-gram flying robots have transformative potential in applications from search and rescue, to precision agriculture, and environmental monitoring. A key gap in achieving autonomous flight for these applications is the low lift-to-weight ratio of flapping wing and quadrotor designs near 1 gram. To close this gap, we propose a helicopter-style design that minimizes size and weight by leveraging the high lift, reliability, and low-voltage of sub-gram motors. We take an important step to enable this goal by designing a light-weight, microfabricated flybar mechanism and tail wing rotor to passively stabilize the Coin-Copter: a helicopter-style robot. A 48 mg flybar is folded from a flat carbon fiber laminate into a 3D mechanism that couples the tilting of the flybar to a change in the angle of attack of the rotors. The Coin-Copter’s flybar uses a novel flexure joint design instead of the ball-in-socket joints common in larger flybars. This flybar achieved a peak damping ratio of 0.528, an 18.9x improvement from our initial design. Compared to a flybarless rotor with a near 0 damping ratio, our flybar-rotor mechanism can maintain a stable roll and pitch with relative deviations <1°. This research focuses on testing the yaw stability of a near-gram flying robot by incorporating and improving on flybar designs, roll-pitch-yaw test setups, and writing robot control software using pulse width modulation to help precisely control the heading of the Coin-Copter.

Recent studies have shown that pedagogical approaches like hands-on lessons, representative and near-peer mentoring, as well as culturally responsive teaching increase Science Technology Engineering and Math (STEM) engagement in classrooms, specifically those with underrepresented minority (URM) students. URM students interested in pursuing STEM show increased engagement and confidence from holistic outreach programs, unfortunately, there is a dearth of URM instructors who also have the necessary technical know-how. However, new AI tools based on Large Language Models (LLMs), like ChatGPT-3.5, have been shown to increase the productivity of software developers, with the largest productivity gains being for non-experts. Therefore, we propose a study on the effects and limitations of LLMs as an educational tool for supporting students and instructors of various skill levels in both facilitating programming classes for URM students and bringing embedded systems projects to completion. We will instruct 40 hours of culturally relevant Arduino course content to 25-35 URM students. We will allow ChatGPT-3.5 to be used as an educational tool without explicitly telling students to use it as a means of understanding perceptions and hesitations around the tool from URM communities. As our lab’s previous research has seen a significant increase in productivity and project completion with the use of LLMs with novice programmers, we aim to see students who choose to use ChatGPT-3.5 program and complete their projects faster than those who choose to not, as well as an implicit understanding of prompt engineering over time. We anticipate that exposure to the tool will cultivate an interest in exploring other AI and LLM opportunities. Lastly, we hope that implementing LLMs within the curriculum will increase the number of available near-peer instructors to teach these courses by aiding content-inexperienced instructors, thus aiding in closing the digital divide.