AI-Driven Robotic PCI Perception-to-Action Framework is a M.Tech project topic for Electrical Engineering. It gives students a clear starting point for research, implementation planning, and documentation.
AI-Driven Robotic PCI Perception-to-Action Framework Project Details
| Abstract |
Robotic Percutaneous Coronary Intervention (PCI) needs very precise control to navigate complex, winding blood vessels safely. This research explores a perception‑to‑action system that automatically steers a coronary guidewire during robotic PCI. It combines real‑time sensor feedback with advanced motion‑planning algorithms so visual or force data can be turned directly into robot actuator commands. The approach models the non‑linear dynamics of the flexible guidewire inside constrained vessels and uses closed‑loop control to reduce the risk of colliding with the vessel wall. Simulations play a key role in testing how robust the control loop is under different anatomical constraints and changing blood‑flow conditions. The project also provides a solid foundation for evaluating
deep‑reinforcement‑learning or supervised‑learning methods in medical robotics. Overall, the work moves from manual catheterization toward intelligent, semi‑autonomous robotic assistance and offers a systematic way to assess trajectory‑tracking accuracy, response latency, and safety margins in simulated coronary environments.
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| Reference Paper |
AI-Driven Robotic PCI: A Perception-to-Action Framework for Coronary Guidewire Control. |
| Domain |
Electrical Engineering |
| Sub-Domain |
Control Systems / Robotics & Automation / Motion Planning |
| PDF Download |
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| Get Help |
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