Stochastic collision detection between deformable models using particle swarm optimization algorithm

An efficient algorithm for detecting collisions between highly deformable mass objects is proposed, which is a combination of newly developed stochastic method and panicle swarm optimization (PSO) algorithm. Firstly, the algorithm samples primitive pairs within the models to construct a discrete binary search space for PSO, by which user can balance performance and detection quality. In order to handle the deformation of models in the object space, a particle update process was added in the beginning of every time step, which handles the dynamic environments problem in search space caused by deformation. The algorithm is also very general that makes no assumption about the input model, which can be without topology information or even be 'polygon soups'. It doesn't need to store additional data structures either, so the memory cost is relatively low. The precision and efficiency evaluation about the algorithm was given, which proved that it might be a reasonable choice for deformable models in stochastic collision detection.