Group for Interventional Robotic and Imaging Systems (IRIS)

Glad to have announced our robot in stereotactic neurosurgery at InnoWing2 yesterday! 😎 Thanks for Prof. Poon and Dr. Chan and also members of the Group, for their support on our research all the way throughout the 10-year R&D journey of our neuro robot! 😘 Press release detail from HKU: https://lnkd.in/gtVeqdbe Flexible endoscopic robot introduced: Agilis Robotics Limited #GroupIRIS #surgicalrobotics #softrobot #roboticneurosurgery #intraoperativeMRI #MRI

Throwback on the MRC Symposium last week 😌 Great workshops & invited talks sessions 🙌 Also, congrats on Xiangkai for the Best Application Award at the Poster session🎉 Multi-Scale Medical Robotics Center Ltd

In May 2024, join #mrcsymposium, hands-on surgical robots' workshops from different projects. Filled with speakers who are the experts in surgical robotics. The Organizing Committee cordially invites you to submit a poster to share your innovative practices, the best poster will be honored with prizes. You may explore more details in: https://lnkd.in/e3aaTfiH Date: 9th -11th May 2024 Venue for Main Symposium: CUHK Medical Centre Venue for Workshops: MRC R&D Lab, Hong Kong Science Park 🛠Register NOW⚙ (Deadline: 22 April 2024)⚠⚠⚠ https://lnkd.in/eKvJpcXz 🛠Call for Poster Submission⚙ (Deadline: 5 April 2024) ⚠⚠⚠ https://lnkd.in/eRpv-AaK #mrccuhk #medtech #innovation #surgical #robotics

Wish you good luck, good health, great success, and good cheer in the new year. Happy Chinese New Year 2024! 🐉 🍊

About our latest intra-op MRI-guided robot in stereotactic neurosurgery 🧠 work in collab with CUHK Medicine 😀

Latest news about Zhuoliang He's latest publication on Advanced Science. 👏👏👏

Jason conferred his MPhil degree at the 211st HKU Congregation Ceremony!! Congrats to ChiHin Mak again!! 🎉

We propose a low-inertia tensegrity joint that is covered/protected by a fiber Bragg grating (FBG)-embedded silicone sheath, with the aim to reinforce the joint motion stability and enable self-contained sensing feedback. A learning-based closed-loop controller is also designed and trained with the proper joint configurations selected by a two-step sampling method. Both the kinematics and static equilibriums of such configurations can be well satisfied. This work is published on Advanced Intelligent Systems (AISY) at https://lnkd.in/gJnKUq6v https://lnkd.in/gxizg5Nc

About our work on TNNLS 💡 The decentralized H2 state-feedback control problem for networked discrete-time systems with positivity constraint is studied. By virtue of positive systems theory and H2 performance analysis, positive decentralized H2 control analysis and synthesis are implemented for networked positive systems. It is worth noting that in contrast to most works, which only provide sufficient synthesis conditions, the problem is studied within a primal-dual framework. The primal-dual characterization gives the necessary and sufficient synthesis conditions, allowing parametrizing the controller gains without imposing any specific structures on the Lyapunov matrix. In other words, one can impose some particular constraints on the controller, such as sparsity, positiveness, and negativeness, along with a free parameter in the computation. Utilizing the derived conditions, a primal-dual iterative algorithm is thus developed for solution, which helps prevent from converging to a local minimum, a limitation often encountered in conventional convex optimization algorithms. To validate the proposed results, two illustrative examples are employed. The stability, positivity, and H2 performance of the closed-loop system are examined, demonstrating that the suggested control strategy effective controls the networked positive systems. Check out our work ⏩ https://lnkd.in/g3tYGm_n

Here shares another work on soft robot control from our group on IEEE Transactions on Robotics 😎 📜 https://lnkd.in/gV5d5SCC We fuse visual information with the sparse strain data collected from a single-core fiber inscribed with fiber Bragg gratings (FBGs) to facilitate continuum robot pose estimation. An improved extreme learning machine algorithm with selective training data updates is implemented to establish and refine the FBG-empowered pose estimator online, improving sensing robustness under moving visual obstacles and varying lighting. Utilizing the fused pose feedback, a hybrid controller incorporating kinematics-based and data-driven algorithms is proposed to accomplish fast convergence with high accuracy. Without requiring fine model-parameter tuning and prior data acquisition, the hybrid controller can improve the target tracking performance with a 52.3%–90.1% error reduction compared with constant-curvature model-based control. #GroupIRIS #surgicalrobotics #softrobot #robotsensing #robotkinematics #control #BraggGratings