Path Planning Algorithm Based on PPO for Indoor Crowd Evacuation

With the frequent occurrence of terrorist attacks, crowd evacuation path planning in indoor public places has received increasing attention. This paper proposes a path planning method based on the Proximal Policy Optimization (PPO) algorithm to improve the efficiency and safety of pedestrian evacuation. The indoor terrorist attack scenario is described, and static obstacles, idle locations, dynamic obstacles, exits, and pedestrians in indoor public places are modeled using a cellular automata model. Subsequently, a feature construction method based on distance information is proposed to construct pedestrian features that represent the escape difficulty of evacuation paths, including shortest path features and safe path features, by combining the distance from pedestrians to exits in non-threatening environments and threat-facing scenarios. Finally, by describing the evacuation path planning problem as a reinforcement learning problem, a reward function based on evacuation efficiency, death penalty and successful escape reward is designed. Based on the feedback from the real-time environment, an evacuation strategy is provided to pedestrians, which in turn realizes the overall optimization of the escape path by the PPO algorithm. Compared with existing field methods, the proposed method can improve the efficiency and safety of crowd evacuation in different simulation scenarios, particularly in complex and high-density environments. The effectiveness of the shortest and safe path features is verified through ablation experiments.