融入情绪模型的人群实时路径规划

目的 人群仿真中高效性和逼真性是群体路径规划的关键问题。已有人群路径规划算法忽略了人群情绪造成的路径选择差异,在大规模实时仿真中仍存在一定的局限性。方法 提出一种融入情绪模型的人群实时路径规划算法(EPP)。使用人格特征理论对人群的情绪进行建模并设定不同情绪所具有的路径选择偏好。在路径建模阶段,通过单次搜索建立全局有向导航图,确定全局层面的可行路径;在路径搜索阶段,提出以期望时间最短为原则的路径规划目标函数,采用局部搜索策略为个体实时规划一条最优或次优的路径。结果 EPP算法可有效地仿真不同场景下大规模人群的路径选择现象;与已有工作的仿真效果和量化指标对比说明了该算法的有效性和高效性;通过不同情绪状态下人群路径选择差异的讨论以及在不同人群运动模型的兼容性实验进一步说明了该算法的健壮性。结论 本文算法具有良好的高效性和健壮性,适用于不同场景下大规模人群路径规划的相关应用。

Real-time emotion-integrated crowd path planning

Objective Effectiveness and being realistic are the essential problems in crowd path planning in crowd simulations. Existing path planning algorithms have limits when applied in large-scale simulation, which ignores the diverse path preferences caused by psychological factors. Method In this paper, we propose a real-time emotion-integrated path planning algorithm(EPP). Based on personality theory, we build an emotion model for crowds and set the diverse path preference for different emotions. For path modeling, we constructed a global directed navigation graph with single-step global search to identify the available global path. For path search, the objective function with the least expected time principle is presented. With this objective function, real-time local search is employed to determine the optimal or suboptimal solution. Result Experiments show that the proposed approach can effectively simulate path planning with a large-scale crowd in different scenes. Compared with previous algorithms, EPP is more effective and efficient. The robustness of the proposed approach is further validated by discussing the differences in crowd path planning at different emotional states. A compatibility experiment is also conducted by integrating the proposed algorithm into different crowd movement models. Conclusion The proposed approach is highly effective and efficient and can be adopted for applications with large-scale crowds and diverse scenarios.