Browsing by Author "Anderson, Chase"

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    AGGRO: Autonomous Gatherer with Guided Retrieval Operations
    (2024-07) Anderson, Chase
    This thesis presents AGGRO: The Autonomous Gatherer with Guided Retrieval Operations, an innovative robotic system designed to enhance object manipulation in cluttered environments. Building on the foundations of Deep Q-Learning (DQL) and advanced reinforcement learning techniques, AGGRO integrates machine learning, robotics hardware, and sophisticated algorithms to address the "Grasping the Invisible" problem at scale. The system employs a combination of primitive synergies to achieve efficient and precise manipulation of occluded objects. Through comprehensive real-world testing and simulation, the thesis explores various explortation policies, dynamic clutter generation, and the impact of structured clutter scenarios on system performance. The results demonstrate a three policy approach to efficiently reveal targets, fully uncover them, and finally singulate to grasp.
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    The Expansion of Digital Microfluidic Systems
    (2021) Anderson, Chase
    Digital microfluidics (DMF) is a technology that allows for movement and manipulation of liquid using electricity. By charging and discharging conductive pads upon a grid, one is able translate, split, and mix droplets to perform conventional wet lab operations. By minimizing handling time, chemical dangers, and laboratory wastes (e.g. pipette tips); DMF stands at the forefront of modern bio-chemical implementations. Most DMF prototypes/platforms utilize a single grid space, and this should change. In hopes to connect 4 ,16 , 64, or n-many grids for that matter; current DMF designs do not have required infrastructure. Each pad must be charged to upwards of 300 volts, so hardware, separate from the brains of the system, is required to facilitate normal operation. Like the heart, DMF platforms operate on beats and must be synchronous. Often, a microcontroller (Arduino, PIC, STM32) is used; however, microcontrollers operate sequentially, meaning each instruction given to the machine must execute one after the other. If one wished to parallelize the process of DMF, a separate computing paradigm must be used. Enter the FPGA (Field Programmable Gate Array). Being able to process multiple lightweight channels of data at a time, the FPGA enables the end user to have thousands if not millions of droplets moving synchronously across hundreds of grid systems. The research task was to find an elegant solution to the problem of parallelizing DMF systems using FPGAs.