Beschreibung
Vision based inference for mobile robots in order to avoid obstacles, is one of the most attracted areas for both domains of Computer Vision and Robotics. Computer Vision, more specifically the vision for intelligent machines enables mobile robots to perceive the external world with 'wisdom'. Therefore Vision based obstacle avoidance has become one of the major research areas of Robotics. Estimating the motion path and predicting the motion behavior of a dynamic object with only single camera (monocular vision) is a real challenge. We realized this can be done by analyzing a sequence of image frames extracted from a live video stream. But, these analytic techniques must be extremely fast in real time processing, since the decision drawn within reasonably short response time is the only 'god' to safeguard the robot & ensure the safety of others in environment! Therefore, we postulate a fuzzy-mathematical model: an Artificial Intelligence approach to achieve the ultimate objective, which has a significant impact in terms of simplicity (reduced complexity) together with efficiency (minimized computational overhead: resource consumption), rather than conventional mathematical modeling.
Autorenporträt
Suneth Pathirana is a researcher in Artificial Intelligence & Robotics.Obtained B.Sc.(C.Sc.)(Sp.)(Hons) from Uva Wellassa University of Sri Lanka in 2010. Then joined University of Colombo, later University of Ruhuna as a Lecturer. Completed M.Sc. in AI at University of Moratuwa in 2013.Currently reading for Ph.D. at MSU, Malaysia.
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