Abstract In human-populated environments, robot navigation requires more than mere obstacle avoidance for safe and comfortable human-robot interaction. Socially aware navigation approaches become vital for deploying mobile service robots in human interactive environments, where the robot operates in interaction with human implicitly or explicitly. These approaches aim to generate human-friendly paths in human-robot interactive environments considering social cues and human behaviour patterns. This paper proposes a social navigation framework for mobile service robots, maintaining humans’ safety and comfort while navigating towards the goal location in human interactive environments. Our main contribution is that the presented social navigation framework is designed to be used in human interactive unknown environments. To achieve this goal, we use a variant of a pedestrian model called Collision Prediction based Social Force model (CP-SFM). This model is particularly developed for low or average density environments and takes the motion of the people tracked in the environment into account during the navigation. The model is employed as a local planner to generate human-friendly plausible routes for our service robot in corridor like indoor environment scenarios. A variety of different extensions and improvements of the conventional social force model are employed in the implementation stage. A novel improvement in producing multi-level mapping, identifying obstacle repulsion points and adopting CP-SFM for application in motion planning as local task solver is presented. The whole framework has been implemented as ROS nodes, and tested both in real world and simulation environments and successfully verified based on the obtained results.

中文翻译：

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人类居住未知环境中辅助机器人的社交导航框架

摘要 在人类居住的环境中，机器人导航需要的不仅仅是避障，以实现安全舒适的人机交互。具有社会意识的导航方法对于在人类交互环境中部署移动服务机器人变得至关重要，在这种环境中，机器人隐式或显式地与人类交互。这些方法旨在考虑社会线索和人类行为模式，在人机交互环境中生成人类友好的路径。本文提出了一种用于移动服务机器人的社交导航框架，在人类交互环境中向目标位置导航的同时保持人类的安全和舒适。我们的主要贡献是所提出的社交导航框架旨在用于人类交互式未知环境。为了实现这一目标，我们使用行人模型的变体，称为基于碰撞预测的社会力模型 (CP-SFM)。该模型专为低密度或平均密度环境而开发，并在导航过程中考虑了环境中被跟踪人员的运动。该模型被用作本地规划器，为我们的服务机器人在走廊等室内环境场景中生成对人类友好的合理路线。在实施阶段对传统社会力量模型进行了各种不同的扩展和改进。提出了在生成多级映射、识别障碍物排斥点和采用 CP-SFM 作为局部任务求解器在运动规划中的应用方面的新改进。整个框架已经实现为ROS节点，