Medical technology, while improving greatly with time, often requires a sacrifice in the form of invasiveness in order to reach target areas within the body, such as the brain, liver, or heart. This project aims to utilize a magnetic, flexible needle design to reach these target areas for surgery and drug administration with minimal invasiveness. The metallic needle tip is guided by an external system consisting of a UR16e robotic arm with a magnetic end effector. As a longer running project, the primary focuses of this research are to develop the system by which the robotic arm guides the needle, investigate and implement fiber Bragg grating sensors as a means of real time path imaging and feedback, and conduct preliminary tests to validate that the needle is accurately controlled by the robotic arm. Testing with different mediums such as gel or phantom tissue, and eventually animal experiments will follow in a future publication due to time constraints.

This project compared two optimization-based formulations for solving multi-robot task allocation problems with tether constraints. The first approach, or the ”Iterative Method,” used the common multiple traveling salesman (mTSP) formulation and implemented an algorithm over the formulation to filter out solutions that failed to satisfy the tether constraint. The second approach, named the ”Timing Formulation,” involved constructing a new formulation specifically designed account for robot timings, including the tether constraint in the formulation itself. The approaches were tested against each other in 10-city simulations and the results were compared. The Iterative Method could provide answers in 1- and 2-norm variations quickly, but its mTSP model formulation broke down and became infeasible at low city numbers. The 1-norm Timing Formulation quickly and reliably produced solutions but faced high computation times in its 2-norm manifestation. Ultimately, while the Timing Formulation is a more optimal method for solving tether-constrained task allocation problems, its reliance on the 1-norm for low computation times causes it to sacrifice some realism.

The objective goal of this research is to maximize the speed of the end effector of a three link R-R-R mechanical system with constrained torque input control. The project utilizes MATLAB optimization tools to determine the optimal throwing motion of a simulated mechanical system, while mirroring the physical parameters and constraints of a human arm wherever possible. The analysis of this final result determines if the kinetic chain effect is present in the theoretically optimized solution. This is done by comparing it with an intuitively optimized system based on throwing motion derived from the forehand throw in Ultimate frisbee.