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Parallel and Cable Robotics

ARAS PACR Group Introduction File

The ARAS Parallel and Cable Robotics (PACR) group is focused on the development of novel cable-driven manipulators and their possible applications. Interdisciplinary research fields such as dynamics and kinematic analysis using classic and modern approaches, development of easily deployable robots through robust controllers, implementation of novel self-calibration algorithms, and establishing modern and multi-sensor perception systems for them are among the active lines of research in this group. The theoretical results of this active research group are also directly incorporated for producing commercial products through the spin-off and startup companies originated from the team. Additionally, PACR exploits the simplicity of cable robots combined with SLAM and perception algorithms to create commercial inspection and imaging robots for various applications.

ARAS AI and VR in Medical Robotics

ARAS AI and VR in the Medical Robotics

This research group implements projects based on artificial intelligence and virtual reality for medical applications while enjoying collaboration of eye surgeons of Farabi Eye Hospital. These projects focus on developing robotic tools and methods for surgical training, training assessment, and diagnosing eye diseases. There are three main projects currently under development in this group, which are detailed on the group website. The requirement to join either of these projects are given as follows.

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Surgical Videos: Detection, Tracking, and Skill Assessment

This project was started in 2017 which is considered on the leading edge of technologies in medical applications, while benefiting from international cooperation. This project aims to separate the visual and motion characteristics of expert, intermediate, and novice surgeons to reach a skill-based feature space for surgical skill transfer. We tend to automate surgical skill assessment and leave it to an artificial intelligence.. By successfully implementing the automatic evaluation of surgical skills based on motion and simulated data of the JIGSAWS dataset, this project entered the automated evaluation of surgical skills from real surgical videos in 2020. To this end, a software and some structures based on deep learning and computer vision have been developed to analyze and evaluate surgical skills. This project also involves detecting and tracking key areas in surgery, such as surgical tools and tissues. The following keywords identify some current issues in this project: AI, Computer Vision, Video Classification, Video Understanding, Image Classification, Object Detection and Tracking, Image & Video Processing, CNN, Python and Qt, PyTorch and TensorFlow.

Medical Images: Diagnosis, Detection and Classification

Through the analysis of medical images, this project aims to diagnose eye diseases. The project is being led in collaboration with Farabi Hospital. One of the challenges facing the medical system is recognizing some eye features, such as Keratoconus, and categorizing them into normal, suspicious and KC categories. While these diagnoses are critical, they take up much time and resources from the medical system. AI and deep learning methods are being developed in this group for image classification and to assist surgeons with a more comprehensive view of medical images. To this end, a balanced dataset for the diagnosis of keratoconus is being collected, from the patients in Farabi Eye Hospital, and by using synthetic data generation by a variational autoencoder (VAE). The results of this project assist surgeons to decide whether to perform vision refractive surgeries on patients. The following keywords identify some current issues in this project to get a better understanding of the workspace: AI, Computer Vision, Image Classification, Image Analyzing, Image Processing, CNN, VAE, Python.

Mixed Reality in Surgery: VR and MR approaches

ARAS Mixed Reality research project is aimed to build a simulator of vitrectomy and cataract surgery using virtual reality to provide a completely similar environment to real surgery for eye surgery residents. The Objective of research in this group will be completed in joint collaboration research with the Surgical Robotic (SR) research group of ARAS. We aim to incorporate the mixed reality simulation tool developed in this group to ARASH:ASiST, the product developed in SR group for Vitrectomy training. The following keywords identify some current issues in this project to get a better understanding of the workspace: Mixed Reality, VR and MR approach in Simulation Environment, Sofa, Unity, and Game engines, Unreal Engine, Blender, UV mapping, Meshing, Texture, Cmake, CUDA, C++.

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Surgical Robotics

ARAS SR Group Introduction File

The surgical robotics group aims at developing new robotics-based technologies for robot-assisted surgery and surgery training applications. This includes the design and integration of mechanical and electrical components as well as the development of innovative control structures for these systems. These robotic systems will enhance the safety and efficiency of medical surgeries which leads to more satisfaction in all of the people dealing with the healthcare systems especially the patients, the surgeons, and the residents. This group has enjoyed the collaboration and consultation of several national and international partners in the fields of engineering and medical science.

Mixed Reality in Surgery

ARAS MR Group Introduction File

Virtual Reality in Medicine is a three-dimensional teaching tool used across the field of healthcare as a means of both education and instruction. Virtual Reality commonly refers to healthcare simulation environments in which learners can experience visual stimuli delivered via computer graphics and other sensory experiences. This advanced technology allows learners to obtain the knowledge and understanding necessary to perform a number of tasks and procedures involving the human body, without ever having to practice on a live patient. Central to this technology is the immersive capacity of Virtual Reality, e.g. the simulated environment surrounds a learner’s perceptual field. This means that the user feels psychologically present in the digital world, rather than in their physical reality. Utilized to educate learners on diagnosis, treatment, rehabilitation, surgery, counseling techniques and more, Virtual Reality in medicine is helping to train the next generation of healthcare professionals. This medical simulation technology has shown to have a number of benefits, such as allowing learners to practice their skills without fear of error causing potentially life-threatening impacts. The Virtual Reality tools still provide the hands-on experience required to acquire a familiarity and comfort in performing procedures, but in a safe and controlled setting. Therefore, as learners make mistakes, they can be thoroughly corrected in real-time and without risk. As Virtual Reality modules still require interaction, skills are able to become second nature before they are applied in real world scenarios. ARAS Mixed Reality research group, is aimed to build a simulator of vitrectomy and cataract surgery using virtual reality to provide a completely similar environment for real surgery for eye surgery residents. The Objective of research in this group will be completed with a joint collaboration research with Surgical Robotic (SR) research group of ARAS. We aim to incorporate the mixed reality simulation tool developed in this group to ARASH:ASiST, the product developed in SR group for Vitrectomy training.

Dynamical Systems Analysis & Control

ARAS Dynamical Systems Analysis & Control Group Introduction File

Two principal aims confront us. Firstly, to study dynamical systems theory, including methods for analyzing differential equations and iterated mappings, which draws on analysis, geometry, and topology. We specially concentrate on one of the most important theories in dynamical systems theory and control theory which comprises various topics such as stability, controllability and observability, robustness, identification, optimality. Secondly, to apply and to tailor aforementioned theories to practical systems. Some of the practical systems which we have recently been dealing with are brain-machine interface systems.