ARAS Autonomous Robotics Research Group

Research interest of the Autonomous Robotics group lies primarily in the field of modern intelligent methods applied in a wide variety of fields from technologies relating driverless cars to autonomous land and aerial robots and surgical robotics. The current research theme in the group relates to the development of autonomous and commercial vehicles by implementation of state-of-the-art algorithms such as deep learning on visual data, in order to firstly develop driver assisting products as well as providing the technological grounds to move toward autonomous vehicles.  Deep estimation from single images, dynamic object detection in 3D environments and obstacle avoidance for autonomous flight are some of the on-going projects of the AR group. 

 Current Projects

Ectasia is a condition in which the human cornea gradually becomes thinner and its slope becomes steeper, and eventually the pressure of the internal fluid of the eye causes the cornea to protrude forward. In general, everyone’s cornea is divided into three groups: Normal, Suspect and KC. Keratoconus is important because it limits performing refractive surgeries such as LASIK and femto. To limit KC usually  preoperative care is performed using advanced imaging devices.

In this project, AI technology is used to diagnose KC categories based on four-maps eye images. in order to use this technology, it is necessary to produce a comprehensive database of medical images in the diagnosis of KC disease and its categories. This framework uses the output data of the Pentacam device, especially the Four Maps Refractive images. The data available at Farabi Hospital will be used to collect this data. In the first stage, the collected data need to be properly labeled and annotated to determine the patient’s health level in one of the three available classes.

The labeling process is very important for the data obtained from different sources because the reliability of the data is one of the important assumptions in creating the proper performance of methods based on artificial intelligence. For this means, the detailed expertise of physicians is needed to label and annotate these images. After the collection and labeling process, the potential challenges of this big data are identified and addressed, and the data and annotations associated with each data are aggregated and a comprehensive database is created to classify images.

Past Projects

Notable Alumni

Ali Agha
Technologist at NASA-JPL, Caltech
Kasra Khosoussi
Postdoctoral Associate - LIDS, MIT
Amirhossein Tamjidi
Texas A&M University
Ehsan Mihankhah
Nanyang Technological University
Alireza Norouzzadeh Ravari
Director of Research Office, Tavanir Co

Publications

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