计算机辅助设计与图形学学报
計算機輔助設計與圖形學學報
계산궤보조설계여도형학학보
Journal of Computer-Aided Design & Computer Graphics
2015年
9期
1775-1785
,共11页
路径规划%疏散仿真%基于智能体的仿真%环境建模
路徑規劃%疏散倣真%基于智能體的倣真%環境建模
로경규화%소산방진%기우지능체적방진%배경건모
path planning%evacuation simulation%agent-based simulation%environment modeling
为了提高虚拟行人在疏散仿真中路径规划的智能性, 在微观仿真框架下提出一种基于个体心理的实时路径规划方法. 该方法根据真实行人的认知模式建立以个体为中心的环境认知域, 动态获取其周围环境的人群密度、危险强度等相关信息;进行路径规划时,采用mental cost综合考虑了疏散时间耗费、路径安全性以及自主行动能力等因素对路径选择的影响, 为每个个体生成一条其主观认为"最优"的路径; 在疏散过程中, 虚拟行人能够根据动态更新的现场信息,通过重规划对初始疏散路径进行调整.基于个体认知域与mental cost在导航图中搜索路径,提出了mental A*搜索算法,可以进行实时规划.最后针对不同疏散场景进行了仿真实验,验证文中方法的有效性与可扩展性.
為瞭提高虛擬行人在疏散倣真中路徑規劃的智能性, 在微觀倣真框架下提齣一種基于箇體心理的實時路徑規劃方法. 該方法根據真實行人的認知模式建立以箇體為中心的環境認知域, 動態穫取其週圍環境的人群密度、危險彊度等相關信息;進行路徑規劃時,採用mental cost綜閤攷慮瞭疏散時間耗費、路徑安全性以及自主行動能力等因素對路徑選擇的影響, 為每箇箇體生成一條其主觀認為"最優"的路徑; 在疏散過程中, 虛擬行人能夠根據動態更新的現場信息,通過重規劃對初始疏散路徑進行調整.基于箇體認知域與mental cost在導航圖中搜索路徑,提齣瞭mental A*搜索算法,可以進行實時規劃.最後針對不同疏散場景進行瞭倣真實驗,驗證文中方法的有效性與可擴展性.
위료제고허의행인재소산방진중로경규화적지능성, 재미관방진광가하제출일충기우개체심리적실시로경규화방법. 해방법근거진실행인적인지모식건립이개체위중심적배경인지역, 동태획취기주위배경적인군밀도、위험강도등상관신식;진행로경규화시,채용mental cost종합고필료소산시간모비、로경안전성이급자주행동능력등인소대로경선택적영향, 위매개개체생성일조기주관인위"최우"적로경; 재소산과정중, 허의행인능구근거동태경신적현장신식,통과중규화대초시소산로경진행조정.기우개체인지역여mental cost재도항도중수색로경,제출료mental A*수색산법,가이진행실시규화.최후침대불동소산장경진행료방진실험,험증문중방법적유효성여가확전성.
Path planning is one of the critical issues of evacuation simulations. We present a real-time path planning approach under the microscopic simulation framework. Based on his/her cognitive ability, a cogni-tive field around each pedestrian is constructed during simulation. The environment information perceived by each individual is recorded in different accuracy in different sub-regions of his/her cognitive field. During path planning, the pedestrian will account factors including time-cost, safety of the path and the crowd flow etc., which we model as mental cost, based on the information provided by the cognitive field to evaluate the priority of each candidate path. An algorithm is developed to estimate the mental cost to select the best evacuation path. During simulation, the cognitive field of each pedestrian will keep updated and the pedes-trian can adjust his/her original evacuation path if necessary. We adopt Mental A* algorithm to search the path from the navigation graph. Experiments regarding different scenarios demonstrate the effectiveness of our approach.