Research On Real-time Path Planning Algorithm Integrated With Emotion Model Of Crowd Animation

Using technology of crowd simulation to forecasting the global trend of movement and the potential changes of crowd can be helpful for the management of public safety, building design, disaster evacuation decision with scientific basis. Among them, path planning which used to guide the individual to move is the key point of virtual crowd simulation. In recent years, the crowd oriented path planning algorithms have been deeply developed, but the algorithms have ignored the influence of emotion on route choice, there are still some limitations in the crowd simulation.In view of the above problems, this thesis puts forward a path planning algorithm integrated with personality theory of psychology to simulation emotional crowd in real-time, the main work is as follows:(1) A real-time path planning algorithm was proposed for emotional crowd. The algorithm consist of three stage which are emotion modeling, global path building and local path search, which is high efficiently and realistically achieve the diversification of large-scale crowd and has good robustness.(2) Three kinds of moods, which are loose, calm and panic, are abstracted by our emotional model based on personality theory of psychology. The emotional model inherited the personality theory’s characteristics of distribution at the same time for showing the diversity of population under different emotional states in a more realistic way.(3) Based on different preferences on paths of these three mood, a novel objective function has been proposed to get real-time path planning which based on the shortest time of expectation. The objective function is only applied to search local node of global environment which greatly reduces the complexity of time by minimize the scale of search problem. At the same time, the preferences on paths of mood ensure the diversity of movement in crowd simulation.The experiments show that our approach can simulate the path planning with large-scale crowd in different scenes effectively. Compared with the previous works, our approach is more efficient. The robustness of our approach is further validated by discussing the differences of crowd path planning with different emotional states. The compatibility experiment is also introduced by integrating our algorithm into different crowd movement models.