๐ Paper: Contact-Aided Navigation of Flexible Robotic Endoscope Using Deep Reinforcement Learning in Dynamic Stomach
๐ฉโ๐ฌ Authors: Chi Kit Ng, Huxin Gao, Tianao Ren, Prof. Jiewen Lai, and Prof. Hongliang Ren
๐ ๐ช๐ต๐ ๐ถ๐ ๐บ๐ฎ๐๐๐ฒ๐ฟ๐:
Navigating flexible robotic endoscopes in the dynamic, deformable stomach environment is a grand challenge. Our proposed Contact-Aided Navigation (CAN) strategy, powered by deep reinforcement learning and force-feedback, achieved:
โข 100% success rate in both static and dynamic simulated stomach environments
โข Average navigation error of just ๐ญ.๐ฒ ๐บ๐บ
โข Robust generalization even under strong external disturbances
This work highlights how ๐ฒ๐บ๐ฏ๐ผ๐ฑ๐ถ๐ฒ๐ฑ ๐๐ ๐ฎ๐ป๐ฑ ๐ฏ๐ถ๐ผ๐บ๐ฒ๐ฐ๐ต๐ฎ๐ป๐ถ๐ฐ๐-๐ถ๐ป๐๐ฝ๐ถ๐ฟ๐ฒ๐ฑ ๐๐๐ฟ๐ฎ๐๐ฒ๐ด๐ถ๐ฒ๐ can transform surgical robotics, enabling safer and more precise navigation in complex clinical environments.
Check the paper at https://lnkd.in/g6KgZTdD
๐ Huge thanks to the team, collaborators, and the broader robotics community for the support and inspiration.
