Amita Kapoor, is Associate Professor in the Department of Electronics, SRCASW, University of Delhi and has been actively teaching neural networks and artificial intelligence for the last 20 years. She completed her masters in Electronics in 1996 and PhD in 2011, during PhD she was awarded prestigious DAAD fellowship to pursue a part of her research work in Karlsruhe Institute of Technology, Karlsruhe, Germany. She was awarded best Presentation Award at the Photonics 2008 international conference. She is an active member of ACM, AAAI, IEEE, and INNS. She has co-authored two books. She has more than 40 publications in international journals and conferences. Her present research areas include ML, AI, Deep Reinforcement Learning and Robotics.
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Introduction Separate Axis Theorem (SAT) is a method to determine whether two convex shapes are intersecting or not. The theorem states that, “Given two convex shapes there exists a line onto which their projections will be separate if and only if they are not intersecting”. It is the most commonly used method for collision detection. SAT also returns the minimum penetration vector, or minimum translation vector, the shortest distance that the colliding object can be moved in order to no longer be colliding. The basic idea is imagine you have two convex shapes, let us consider two triangles as given in fig 1. We shine light on them from different angles and observe shadows on the wall perpendicular to the direction of light, if doing this work around the shapes we do not find a gap in the shadows, then the objects are colliding. But, if we are able find a gap, then they are not intersecting/colliding. Algorithm: The first question that arises while implementing SAT