Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Francesco Cursi"'
Publikováno v:
IEEE Access, Vol 10, Pp 5012-5023 (2022)
Robot design is a major component in robotics, as it allows building robots capable of performing properly in given tasks. However, designing a robot with multiple types of parameters and constraints and defining an optimization function analytically
Externí odkaz:
https://doaj.org/article/42a247e11a844e21833bce7ee3406501
Publikováno v:
Applied Sciences, Vol 11, Iss 11, p 4746 (2021)
Robots have been predominantly controlled using conventional control methods that require prior knowledge of the robots’ kinematic and dynamic models. These controllers can be challenging to tune and cannot directly adapt to changes in kinematic st
Externí odkaz:
https://doaj.org/article/343a86bfa77543338857710a715f134a
Publikováno v:
Robotics, Vol 9, Iss 3, p 68 (2020)
Accurate kinematic models are essential for effective control of surgical robots. For tendon driven robots, which are common for minimally invasive surgery, the high nonlinearities in the transmission make modelling complex. Machine learning techniqu
Externí odkaz:
https://doaj.org/article/a7fbcfd9f5e24b4b8a0f6a98579c3944
Autor:
Xiaotong Guo, Guang-Zhong Yang, Eric M. Yeatman, Baoru Huang, Francesco Cursi, Benny Lo, Weibang Bai
Publikováno v:
IEEE Transactions on Medical Robotics and Bionics. 4:339-342
Tendon-driven flexible surgical robots are normally suffering from the inaccurate modelling and imprecise motion control problems due to the nonlinearities of tendon transmission. Learning-based approaches are experimental data-driven with uncertaint
Publikováno v:
IEEE Transactions on Automation Science and Engineering. :1-16
Publikováno v:
2022 International Conference on Advanced Robotics and Mechatronics (ICARM).
Publikováno v:
IEEE Robotics and Automation Letters. 6:2642-2649
In order to guarantee precision and safety in robotic surgery, accurate models of the robot and proper control strategies are needed. Bayesian Neural Networks (BNN) are capable of learning complex models and provide information about the uncertaintie
Robots for minimally invasive surgery are becoming more and more complex, due to miniaturization and flexibility requirements. The vast majority of surgical robots are tendon-driven and this, along with the complex design, causes high nonlinearities
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::67cbe2cbc9812d8b643cee027fc1e918
http://hdl.handle.net/10044/1/102818
http://hdl.handle.net/10044/1/102818
Due to the increasing complexity of robotic structures, modelling robots is becoming more and more challenging, and analytical models are very difficult to build. Machine learning approaches have shown great capabilities in learning complex mapping a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::54c9a7c89c1bc66709c532d542c58b04
http://hdl.handle.net/10044/1/102811
http://hdl.handle.net/10044/1/102811
In Minimally Invasive Robotic Surgery (MIRS), the surgical instrument is usually inserted inside the patient’s body through a small incision, which acts as a Remote Center of Motion (RCM). Serial-link manipulators can be used as macro robots on whi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::27eb75c1f0c961c47a90f920f73dd08c
http://hdl.handle.net/10044/1/96516
http://hdl.handle.net/10044/1/96516