首页> 外文会议>Intelligent System Design and Engineering Application (ISDEA), 2012 Second International Conference on >Detection of Collision and Self-Collision Using QPSO for Deformable Models
【24h】

Detection of Collision and Self-Collision Using QPSO for Deformable Models

机译:使用QPSO检测可变形模型的碰撞和自碰撞

获取原文
获取原文并翻译 | 示例

摘要

In order to improve the speed of deformable objects collision and self-collision detection, proposing a new kind of stochastic collision method which is based on Quantum-behaved Particle Swarm Optimization. In this new algorithm the collision detection problem is treated as a kind of problem which is similar with Dynamic and Multi-objective Optimization Problem(MOP) where as many of the collision pairs satisfying the collision conditions are detected in certain time interval, notice that the detected collision pairs are not necessarily the globally optimal solution. For this problem that is similar with MOP, the iteration searching process for quantum-behaved particle has been optimized, in this algorithm, once a new collision pair satisfying the condition is detected, then the next searching will be converge towards the latest detected collision pair who satisfying the condition. This strategy significantly improved the searching ability for the satisfied collision pairs detection in the limited time interval, and there is no need for clustering, merger, division and any other operations, experiment results show that the efficiency of this algorithm is much better than the similar algorithms.
机译:为了提高可变形物体碰撞和自碰撞检测的速度,提出了一种基于量子行为粒子群优化的新型随机碰撞方法。在该新算法中,将碰撞检测问题视为一种问题,与动态和多目标优化问题(MOP)相似,在一定时间间隔内检测到满足碰撞条件的许多碰撞对,请注意检测到的碰撞对不一定是全局最优解。针对与MOP相似的问题,对量子行为粒子的迭代搜索过程进行了优化,在该算法中,一旦检测到满足条件的新碰撞对,则下一个搜索将收敛到最新检测到的碰撞对谁满足条件。该策略在有限的时间间隔内显着提高了对满足的碰撞对检测的搜索能力,并且不需要聚类,合并,分割等任何操作,实验结果表明,该算法的效率远优于同类算法。算法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号