This paper introduces a novel approach that integrates future closest point predictions into the distance constraints of a collision avoidance controller, leveraging convex hulls with closest point distance calculations. By addressing abrupt shifts in closest points, this method effectively reduces collision risks and enhances controller performance. Applied to an Image Guided Therapy robot and validated through simulations and user experiments, the framework demonstrates improved distance prediction accuracy, smoother trajectories, and safer navigation near obstacles.

Cyber-Physical Systems are designed and developed using multi-disciplinary teams.Handovers from one discipline to another often occur using text documents written in natural language, which can be imprecise, ambiguous, and lead to errors.To improve this situation, we created a textual Domain Specific Language with live graphical feedback to enhance the handover between mechanical and mechatronic engineers working on medical robots at Philips IGT.The Domain Specific Language formalizes the system description and provides immediate live graphical feedback to prevent mistakes from being made, such as editing the wrong physical parts and by visualizing the differences of two versions of a system.In addition, our approach leverages multiple industry standards and it enables bi-directional navigation between languages.

This paper presents a Bayesian optimization framework for the automatic tuning of shared controllers which are defined as a Model Predictive Control (MPC) problem. The proposed framework includes the design of performance metrics as well as the representation of user inputs for simulation-based optimization. The framework is applied to the optimization of a shared controller for an Image Guided Therapy robot. VR-based user experiments confirm the increase in performance of the automatically tuned MPC shared controller with respect to a hand-tuned baseline version as well as its generalization ability.

The paper presents a new method for data-driven feedforward compensation of static and quasi-static forces acting on a multi-axis medical robotic manipulator. The proposed approach uses a look-up current calibration table (CCT) and an adaptive algorithm updating the CCT to ensure that the manipulator maintains accurate, fast, and safe performance over time. The key aspect of our control strategy is called data assimilation step, which involves modelling the CCT using an approximating function. We use the NURBS (Non-uniform rational basis spline) technique, which has desirable properties such as high accuracy and flexibility in approximating and even interpolating complex functions. The technique allows the manipulator to compensate for external disturbances such as gravity, friction and gear or cabling resistance. This can improve the precision and reduce the downtime of the manipulator due to periodic feedforward recalibration.

At Philips IGT, we develop and produce interventional X-ray systems. For a controller in these systems, we have an approximately five years old domain specific language. Like general programming languages, domains specific languages also evolve. These languages co-evolve together with their domain. The language used at IGT was initially created for one system instance. Because of our positive experiences with the language, we want to evolve the language to support a family of systems. In this paper, we report on our experiences with the modifications we made to the original language. We made these changes preserving the behavior of the existing system instance. To prevent confidentiality issues, we use a Lego robot in our examples.