The majority of drones are extremely simple, their functions include flight and sometimes recording video and audio. While drone technology has continued to improve these functions, particularly flight, additional functions have not been added to mainstream drones. Although these basic functions serve as a good framework for drone designs, it is now time to extend off from this framework. With this Honors Thesis project, we introduce a new function intended to eventually become common to drones. This feature is a grasping mechanism that is capable of perching on branches and carrying loads within the weight limit. This concept stems from the natural behavior of many kinds of insects. It paves the way for drones to further imitate the natural design of flying creatures. Additionally, it serves to advocate for dynamic drone frames, or morphing drone frames, to become more common practice in drone designs.
The research presented in this Honors Thesis provides development in machine learning models which predict future states of a system with unknown dynamics, based on observations of the system. Two case studies are presented for (1) a non-conservative pendulum and (2) a differential game dictating a two-car uncontrolled intersection scenario. In the paper we investigate how learning architectures can be manipulated for problem specific geometry. The result of this research provides that these problem specific models are valuable for accurate learning and predicting the dynamics of physics systems.<br/><br/>In order to properly model the physics of a real pendulum, modifications were made to a prior architecture which was sufficient in modeling an ideal pendulum. The necessary modifications to the previous network [13] were problem specific and not transferrable to all other non-conservative physics scenarios. The modified architecture successfully models real pendulum dynamics. This case study provides a basis for future research in augmenting the symplectic gradient of a Hamiltonian energy function to provide a generalized, non-conservative physics model.<br/><br/>A problem specific architecture was also utilized to create an accurate model for the two-car intersection case. The Costate Network proved to be an improvement from the previously used Value Network [17]. Note that this comparison is applied lightly due to slight implementation differences. The development of the Costate Network provides a basis for using characteristics to decompose functions and create a simplified learning problem.<br/><br/>This paper is successful in creating new opportunities to develop physics models, in which the sample cases should be used as a guide for modeling other real and pseudo physics. Although the focused models in this paper are not generalizable, it is important to note that these cases provide direction for future research.
The use of enzyme-catalyst interfaces is underexplored in the field of biocatalysis, particularly in studies on enabling novel reactivity of enzymes. For this thesis, the HaloTag® protein tagging platform was proposed as a bioconjugation method for a pinacol coupling reaction using lipases, as a model for novel reactivities proceeding via ketyl radical intermediates and hydrogen-bonding-facilitated redox attenuation. After an initial lipase screening of 9 lipases, one lipase (Candida rugosa) was found to perform the pinacol coupling of p-anisaldehyde under standard conditions (fluorescein and 530nm light, 3% yield). Based on a retrosynthetic analysis for the photocatalyst-incorporated HaloTag® linker, the intermediates haloamine 1 and aldehyde 6 were synthesized. Further experiments are underway or planned to complete linker synthesis and conduct pinacol coupling experiments with a bioconjugated system. This project underscores the promising biocatalytic promiscuity of lipases for performing reactions proceeding through ketyl radical intermediates, as well as the underdeveloped potential of incorporating bioengineering principles like bioconjugation into biocatalysis to overcome kinetic barriers to electron transfer and optimize biocatalytic reactions.