Daniyal Maroufi

Yash Kulkarni, Mobina Tavangarifard, Daniyal Maroufi et al.

This paper introduces a novel shape-sensing approach for Concentric Tube Steerable Drilling Robots (CT-SDRs) based on Optical Frequency Domain Reflectometry (OFDR). Unlike traditional FBG-based methods, OFDR enables continuous strain measurement along the entire fiber length with enhanced spatial resolution. In the proposed method, a Shape Sensing Assembly (SSA) is first fabricated by integrating a single OFDR fiber with a flat NiTi wire. The calibrated SSA is then routed through and housed within the internal channel of a flexible drilling instrument, which is guided by the pre-shaped NiTi tube of the CT-SDR. In this configuration, the drilling instrument serves as a protective sheath for the SSA during drilling, eliminating the need for integration or adhesion to the instrument surface that is typical of conventional optical sensor approaches. The performance of the proposed SSA, integrated within the cannulated CT-SDR, was thoroughly evaluated under free-bending conditions and during drilling along multiple J-shaped trajectories in synthetic Sawbones phantoms. Results demonstrate accurate and reliable shape-sensing capability, confirming the feasibility and robustness of this integration strategy.

Daniyal Maroufi, Omid Rezayof, Farshid Alambeigi

The Python robotics ecosystem faces a challenge: while many libraries exist for rigid body transformations, few are both lightweight and mathematically strict. This paper introduces SE3Kit, a lightweight Python library efficient operations on the Special Euclidean Group SE(3) and the Special Orthogonal Group SO(3). Unlike established frameworks that require heavy dependencies (e.g., SpatialMath, PyPose) or general tools that lack robotics-specific features (e.g., SciPy), SE3Kit targets the gap between these extremes. It is designed for embedded deployment, rapid prototyping, and education while providing rigorous mathematical implementation. It provides a pure-Python, NumPy-only implementation of Lie Group operations, without the overhead of deep learning or other visualization software.

Mahdi Abdollah Chalaki, Daniyal Maroufi, Mahdi Robati et al.

Despite the recent success in data-driven fault diagnosis of rotating machines, there are still remaining challenges in this field. Among the issues to be addressed, is the lack of information about variety of faults the system may encounter in the field. In this paper, we assume a partial knowledge of the system faults and use the corresponding data to train a convolutional neural network. A combination of t-SNE method and clustering techniques is then employed to detect novel faults. Upon detection, the network is augmented using the new data. Finally, a test setup is used to validate this two-stage methodology on a centrifugal pump and experimental results show high accuracy in detecting novel faults.