9 accepted papers
Playing table tennis with a human player is a challenging robotic task due to its dynamic nature. Despite a number of researches being devoted to developing robotic table tennis systems, most of the works have demanding hardware requirements and ignore safety measures when generating the swing stoke…
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 and have widely been used in robot model learning a
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 in the system which are difficult to model analyti
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 uncertainties of the learned system. Model Predictive Control
Robot modelling is an essential part to properly understand how a robotic system moves and how to control it. The kinematic model of a robot is usually obtained by using Denavit-Hartenberg convention, which relies on a set of parameters to describe the end-effector pose in a Cartesian space. These p…
In robotically assisted surgical procedures the surgical tool is usually inserted in the patient’s body through a small incision, which acts as a constraint for the motion of the robot, known as remote center of Motion (RCM). The location of the insertion point on the patient’s body has huge effects…
In fields such as minimally invasive surgery, effective control strategies are needed to guarantee safety and accuracy of the surgical task. Mechanical designs and actuation schemes have inevitable limitations such as backlash and joint limits. Moreover, surgical robots need to operate in narrow pat…
In the past few decades machine learning and data analysis have been having a huge growth and they have been applied in many different problems in the field of robotics. Data are usually the result of sensor measurements and, as such, they might be subjected to noise and outliers. The presence of ou…
Workspace analysis is essential for robotic manipulators, which helps researchers study, evaluate, and optimize their designs based on specific criteria with due consideration of ergonomics and usability. Although workspace analysis is a common research topic, current solutions provide design-specif