Generalized Simulation Study of Autonomous Obstacle Avoidance Based on Game Non-Player Character

With the rapid development of artificial intelligence technology, unmanned delivery services are increasingly used in restaurants, hotels and other scenes. In this study, for the autonomous obstacle avoidance problem in unmanned delivery service, a generalized simulation experiment method for autonomous obstacle avoidance based on game Non-Player Character (NPC) is proposed. Through simulation research and experimental verification, the obstacle avoidance strategy of the game NPC is innovatively applied to simulate and optimize the obstacle avoidance behavior of unmanned delivery robots in complex environments. This study uses an optimized You Only Look Once v5 (YOLOv5) model as the core of NPC visual processing, increasing processing speed to 21.7 ms. Compared with benchmark models such as YOLOv5, this model significantly improves the efficiency of simulation experiments while maintaining high recognition precision. Based on this model, this study designs and implements a series of efficient decision-making mechanisms to nsure that NPCs can effectively avoid obstacles in simulated scenarios. In addition, this study developed a path planning and decision-making system suitable for unmanned delivery robots, ensuring that robots can operate safely and efficiently in complex environments. The research results provide a solid technical foundation for the continuous improvement and widespread promotion of unmanned delivery services. They also promote the research and application of autonomous obstacle avoidance technology based on game NPCs in the field of unmanned delivery, laying an important theoretical foundation for the development of unmanned delivery technology.