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. 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. 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. The proposed approach is highly effective and efficient and can be adopted for applications with large-scale crowds and diverse scenarios.