Mobile robots have become more and more popular in large-scale and crowded environments, such as airports, shopping malls, etc. However, due to sparse landmarks and crowd noise, localization in this environment is a great challenge. Furthermore, it is unreliable for the robot to navigate safely in crowds while considering human comfort. Thus, how to navigate safely with localization precision in that environment is a critical problem. To solve this problem, we proposed a curiosity-based framework that can find an effective path with the consideration of human comfort and crowds, localization uncertainty, and the cost-to-go to the target. Three parts are involved in the proposed framework: the distance assessment module, the Curiosity for Positive Content (CPC), namely information-rich areas, and the Curiosity for Negative Content (CNC), namely crowded areas. CPC is introduced when the real-time localization uncertainty evaluation is not satisfied. This factor is predicted through the propagation of uncertainty along the candidate trajectory to provoke the robot to approach localization-referenced landmarks. The Human Comfort and Crowd Density Map (HCCDM) based on the Gaussian Mixture Model (GMM) is established to calculate CNC, which drives the robot to bypass the crowd and consider human comfort. The evaluation is conducted in a series of large-scale and crowded environments. The results show that our method can find a feasible path that can consider the localization uncertainty while simultaneously avoiding the crowded area.

翻译：移动机器人在大型密集人群环境（如机场、商场等）中的普及程度日益提高。然而，由于稀疏路标和人群噪声的存在，在此类环境中实现定位是一项巨大挑战。此外，在兼顾人类舒适度的前提下确保机器人在人群中安全导航亦不可靠。因此，如何在保证定位精度的同时实现安全导航成为关键问题。为解决该问题，我们提出了一种基于好奇心的框架，该框架能够在综合考虑人类舒适度、人群密度、定位不确定性以及目标距离成本的基础上规划有效路径。该框架包含三个模块：距离评估模块、正向内容好奇心模块（即信息丰富区域）与负向内容好奇心模块（即密集区域）。当实时定位不确定性评估不满足要求时引入CPC模块，通过沿候选轨迹传播不确定性预测该因子，促使机器人趋近定位参考路标。基于高斯混合模型建立人类舒适度与人群密度图以计算CNC模块，驱动机器人规避人群并兼顾人类舒适度。在系列大规模密集人群环境中的评估结果表明，本方法能够找到兼顾定位不确定性同时自动规避密集区域的可行路径。