Research On Collision Detection Algorithm Of Snake Model Based On Fusion Intelligent Optimization Algorithm

Collision detection is an important part of computer graphics,computer virtual reality and many other fields.With the rapid development of the Internet and virtual reality technology,virtual fitting room、robot path planning and other fields have become the mainstream industries.However,due to the rapid development of these industries,there shows some internal problems.For example,distortion phenomenon appears in the fitting room.At present,the more large-scale complex scene,the stricter the requirement of real-time and realistic for the collision process will become,which makes it especially important that how to improve the efficiency of collision detection quickly and object authenticity.Focused on these specific problems,this paper makes an in-depth study from the following aspects,the main works include:(1)Analyze and summarize all kinds of collision detection algorithms and swarm intelligence optimization algorithms,then find the problems of the collision detection efficiency and real-time performance;(2)In order to make up for the defect of particle swarm optimization algorithm which is easy to fall into local convergence,we put forward a multi linear particle swarm algorithm,and make it combined with the Snake model so as to make up for the Snake model cavity defects of convergence,then apply the improved Snake model to the update of bounding box.Via the experiments,the algorithm improves the detection efficiency of the collision detection algorithm;(3)Based on genetic algorithm and simulated annealing algorithm(GASA),stipulate initial contour position of Snake model and the populations of chromosome coding of genetic algorithm,optimize the defect of weak edge overflow of Snake model,so as to obtain the updated bounding box rapidly and simulate the image object deformation area.The simulation experiments prove that the feasibility of this algorithm in the scene.