虚拟海战场的三维场景生成与实时显示是构成虚拟海战场视景仿真系统的重要部分,包括几何形体建模、纹理映射以及视觉效果处理等内容。以Mu ltiG en C reator为建模平台,通过完成一个仿真模型讨论了解决建模逼真度与仿真实时性矛盾的一...虚拟海战场的三维场景生成与实时显示是构成虚拟海战场视景仿真系统的重要部分,包括几何形体建模、纹理映射以及视觉效果处理等内容。以Mu ltiG en C reator为建模平台,通过完成一个仿真模型讨论了解决建模逼真度与仿真实时性矛盾的一些措施;基于Vega仿真软件环境开发了海洋战场中的各种复杂场景特效,解决了非视觉物理量的可视化问题;利用V isua l C++开发了视景仿真程序,解决了视景驱动,自动视点切换及仿真记录回放等具体问题。仿真结果表明,该系统较好地满足了视景仿真实时性和逼真度的要求。展开更多
The rapid development of intelligent navigation drives the rapid accumulation of ocean data,and the ocean science has entered the era of big data.However,the complexity and variability of the ocean environments make s...The rapid development of intelligent navigation drives the rapid accumulation of ocean data,and the ocean science has entered the era of big data.However,the complexity and variability of the ocean environments make some data unavailable.It makes ocean target detection and the unmanned surface vehicle(USV)intelligent control process in ocean scenarios face various challenges,such as the lack of training data and training environment.Traditional ocean image data collection method used to capture images of complex ocean environments is costly,and it leads to a serious shortage of ocean scene image data.In addition,the construction of an autonomous learning environment is crucial but time-consuming.In order to solve the above problems,we propose a data collection method using virtual ocean scenes and the USV intelligent training process.Based on virtual ocean scenes,we obtain rare images of ocean scenes under complex weather conditions and implement the USV intelligent control training process.Experimental results show that the accuracy of ocean target detection and the success rate of obstacle avoidance of the USV are improved based on the virtual ocean scenes.展开更多
文摘虚拟海战场的三维场景生成与实时显示是构成虚拟海战场视景仿真系统的重要部分,包括几何形体建模、纹理映射以及视觉效果处理等内容。以Mu ltiG en C reator为建模平台,通过完成一个仿真模型讨论了解决建模逼真度与仿真实时性矛盾的一些措施;基于Vega仿真软件环境开发了海洋战场中的各种复杂场景特效,解决了非视觉物理量的可视化问题;利用V isua l C++开发了视景仿真程序,解决了视景驱动,自动视点切换及仿真记录回放等具体问题。仿真结果表明,该系统较好地满足了视景仿真实时性和逼真度的要求。
基金This work was supported by the National Natural Science Foundation of China[61991415,91746203].
文摘The rapid development of intelligent navigation drives the rapid accumulation of ocean data,and the ocean science has entered the era of big data.However,the complexity and variability of the ocean environments make some data unavailable.It makes ocean target detection and the unmanned surface vehicle(USV)intelligent control process in ocean scenarios face various challenges,such as the lack of training data and training environment.Traditional ocean image data collection method used to capture images of complex ocean environments is costly,and it leads to a serious shortage of ocean scene image data.In addition,the construction of an autonomous learning environment is crucial but time-consuming.In order to solve the above problems,we propose a data collection method using virtual ocean scenes and the USV intelligent training process.Based on virtual ocean scenes,we obtain rare images of ocean scenes under complex weather conditions and implement the USV intelligent control training process.Experimental results show that the accuracy of ocean target detection and the success rate of obstacle avoidance of the USV are improved based on the virtual ocean scenes.
