Wearable Computer Systems The goal is to integrate circuits into nonwoven textiles to create wearable textile printed circuit boards and systems. This technology will be applied towards wearable computing applications like physiological monitoring garments, textile RFID, and human-robot distributed networks. Our research adopts the technologies used in the polymer thick film (PTF) industry and adapts and applies them to nonwoven textiles. Instead of weaving or knitting conductive yarns with fabrics, we are currently screen printing conductive inks onto novel nonwoven textile substrates produced in the College of Textiles at NC State University.
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Amorphous Formation Control with Intelligent Competition The goal of this project is to establish and control formation movement of an EvBot II colony without explicitly stating which position each EvBot II is to maintain. Each EvBot II will compete for its position in the formation based on an internal heuristic that is a function of: (1) the location of the EvBot II, (2) the desired formation, (3) the proximity of the EvBot II to the formation configuration, and (4) an internal self-diagnostic control algorithm.
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Robot Interactions with Randomly Distributed Sensor Networks Our goal is to integrate circuits into nonwoven textiles to create textile printed circuit boards. This technology will be applied towards wearable computing applications like physiological monitoring garments, textile RFID, and human-robot distributed networks. Our research adopts the technologies used in the polymer thick film (PTF) industry and adapts and applies them to nonwoven textiles. Instead of weaving or knitting conductive yarns with fabrics, we are currently screen printing conductive inks onto nonwoven textile substrates.
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Sensorimotor Integration and its Effects on Learning and Control of Autonomous Mobile Robots Current work focuses on determining methods of integrating multiple diverse sensors, and corresponding motor states, into a unified framework for learning and control. Initial implementation will be performed on the EvBot II robotic platform. Through effective sensorimotor integration, learning time will be reduced, and the learned control systems will exhibit more robustness of control and increased generalizability.