The objective of this work is to expand upon previous works, considering socially acceptable behaviours within robot navigation and interaction, and allow a robot to closely approach static and dynamic individuals or groups. The space models developed in this dissertation are adaptive, that is, capable of changing over time to accommodate the changing circumstances often existent within a social environment. The space model's parameters' adaptation occurs with the end goal of enabling a close interaction between humans and robots and is thus capable of taking into account not only the arrangement of the groups, but also the basic characteristics of the robot itself. This work also further develops a preexisting approach pose estimation algorithm in order to better guarantee the safety and comfort of the humans involved in the interaction, by taking into account basic human sensibilities. The algorithms are integrated into ROS's navigation system through the use of the $costmap2d$ and the $move\_base$ packages. The space model adaptation is tested via comparative evaluation against previous algorithms through the use of datasets. The entire navigation system is then evaluated through both simulations (static and dynamic) and real life situations (static). These experiments demonstrate that the developed space model and approach pose estimation algorithms are capable of enabling a robot to closely approach individual humans and groups, while maintaining considerations for their comfort and sensibilities.

翻译：本研究旨在扩展先前工作，将社会可接受行为纳入机器人导航与交互范畴，使机器人能够近距离接近静态或动态的单人及群体。本文提出的空间模型具有自适应性，即能够随时间变化以适应社交环境中常出现的动态情境。该空间模型参数的调整以促进人机近距离交互为最终目标，因此不仅能考虑群体布局，也能兼顾机器人自身的基本特征。本研究进一步改进了现有的接近位姿估计算法，通过纳入人类基本感受以更好地保障参与交互人员的安全与舒适。利用$costmap2d$和$move\_base$软件包，相关算法被集成至ROS导航系统。通过数据集对比评估先前算法对空间模型自适应性进行测试。随后通过仿真实验（包含静态与动态场景）及真实场景实验（静态场景）对完整导航系统进行评估。实验证明：所开发的空间模型与接近位姿估计算法能使机器人近距离接近单人及群体，同时兼顾其舒适性与感受。