The presentation explored the challenges and opportunities in motion generation and perception for flexible robots operating in minimally invasive surgical environments. Prof. Ren emphasized the importance of image-guided robotic systems in enhancing surgical precision, flexibility, and repeatabilityโwhile acknowledging the complexities these systems introduce in development.
He shared recent advances from our lab in intelligent motion planning and perception, aiming to enable smart micro-imaging and guided robotic interventions. The proposed remote robotic system is tailored for surgical applications, empowering clinicians with multi-modal sensing and continuous motion generation for dexterous operations.
Tinghua Zhang, Sishen YUAN et al. for “PneumaOCT: Pneumatic optical coherence tomography endoscopy for targeted distortion-free imaging in tortuous and narrow internal lumens”, a collaboration between CUHK ABI Lab (https://lnkd.in/gUuzQqDt) and RENLab (labren.org),
published in Science Advances (DOI: 10.1126/sciadv.adp3145).
“ETSM: Automating Dissection Trajectory Suggestion and Confidence Map-Based Safety Margin Prediction for Robot-assisted Endoscopic Submucosal Dissection”, accepted at #ICRA2025 (arXiv preprint: arXiv:2411.18884).
๐ Congratulations to our brilliant team members on these well-deserved recognitions!
Additionally, Prof. Hongliang Ren delivered an insightful talk, “Endoscopic Multisensory Navigation with Soft Flexible Robotics”, highlighting the latest advancements in endoscopic navigation and soft medical robotics.
๐ In this paper, we unveil an innovative autonomous palpation-based acquisition strategy – RASEC, designed for the tracheal region. RASEC predicts the next acquisition point interactively, maximizing expected information and minimizing palpation procedure costs. By leveraging a Gaussian Process (GP) to model tissue hardness distribution and anatomical information as a guiding input for medical robots, RASEC revolutionizes robot-assisted subtasks in tracheotomy.
๐ก We introduce a dynamic tactile sensor based on resonant frequency to measure tissue hardness at millimeter-scale precision, ensuring secure interactions. By exploring kernel fusion techniques blending Squared Exponential (SE) and Ornstein-Uhlenbeck (OU) kernels, and optimizing Bayesian search with larynx anatomical data, we enhance exploration efficiency and accuracy.
๐ฌ Our research considers new factors like tactile sensor movement and robotic base rotation in the acquisition strategy. Simulation and physical phantom experiments demonstrate a remarkable 53.1% reduction in sensor movement and 75.2% reduction in base rotation, with superior algorithmic performance metrics (average precision 0.932, average recall 0.973, average F1 score 0.952) and minimal distance errors (0.423 mm) at a high resolution of 1 mm.
๐ The results showcase RASEC’s excellence in exploration efficiency, cost-effectiveness, and incision localization accuracy in real robot-assisted tracheotomy procedures.
This collaborative work is achieved by WENCHAO YUE, Fan Bai, Jianbang Liu, and Prof Hongliang Ren from The Chinese University of Hong Kong, Prof Feng Ju from Nanjing University of Aeronautics and Astronautics, Prof Max Q.-H. Meng from Southern University of Science and Technology, and Dr. Chwee Ming Lim from Singapore General Hospital.
