Computer Aided Mechanical dynAmic System Laboratory
Research at CAMAS Lab centers on the modeling, analysis, and control of dynamic systems across mechanical, electrical, hydraulic, and pneumatic domains. A key focus is on advancing the understanding of energy-domain interactions, developing unified physical modeling frameworks, and designing control architectures to effectively address complex nonlinearities and inter-domain dependencies.
The primary goal is to enhance intricate mechatronic systems through the development of advanced robotic platforms possessing high adaptability, precision, and intuitive interaction. These platforms are aimed at advancing industrial automation and optimizing manufacturing processes, ensuring robust operation in real-world environments.
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Robot Mechanism Design & Control
Robotic mechanism and control research at CAMAS Lab addresses the design and implementation of systems suited for collaborative, industrial, and semiconductor applications. Our work spans high-load hydraulic manipulators, high-precision wafer transfer robots, soft pneumatic actuators, and advanced variable-stiffness mechanisms. We design robotic systems from the ground up including mechanical development, dynamic modeling, and real-time control to achieve both precision and safety in demanding environments. Research topics include real-time dynamic compensation for hydraulic manipulators, vibration suppression and trajectory optimization for wafer-handling systems, pneumatic rotary actuators modeled as nonlinear soft systems, multi-curvature and variable-stiffness grippers for delicate object manipulation, and geometry-based design approaches enabling configurable soft robots without complex control. Through these efforts, we develop robust, high-performance robotic platforms that support reliable operation in industrial and collaborative settings.
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Robot/Environment Interactions
Research on robot-environment interaction at CAMAS Lab focuses on enhancing robotic performance in contact-rich tasks by developing control strategies for physical human-robot interaction, collaborative manipulation, and adaptive contact with uncertain environments.
Our work investigates how robots can interpret, predict, and safely respond to dynamic, unstructured interactions. This includes intent estimation frameworks that model human motion in real time, enabling robots to compensate for unknown dynamics and follow user intent during cooperative tasks.
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