The capability of a mobile robot to efficiently and safely perform complex missions is limited by its knowledge of the environment, namely the situation. Advanced reasoning, decision-making, and execution skills enable an intelligent agent to act autonomously in unknown environments. Situational Awareness (SA) is a fundamental capability of humans that has been deeply studied in various fields, such as psychology, military, aerospace, and education. Nevertheless, it has yet to be considered in robotics, which has focused on single compartmentalized concepts such as sensing, spatial perception, sensor fusion, state estimation, and Simultaneous Localization and Mapping (SLAM). Hence, the present research aims to connect the broad multidisciplinary existing knowledge to pave the way for a complete SA system for mobile robotics that we deem paramount for autonomy. To this aim, we define the principal components to structure a robotic SA and their area of competence. Accordingly, this paper investigates each aspect of SA, surveying the state-of-the-art robotics algorithms that cover them, and discusses their current limitations. Remarkably, essential aspects of SA are still immature since the current algorithmic development restricts their performance to only specific environments. Nevertheless, Artificial Intelligence (AI), particularly Deep Learning (DL), has brought new methods to bridge the gap that maintains these fields apart from the deployment to real-world scenarios. Furthermore, an opportunity has been discovered to interconnect the vastly fragmented space of robotic comprehension algorithms through the mechanism of Situational Graph (S-Graph), a generalization of the well-known scene graph. Therefore, we finally shape our vision for the future of robotic Situational Awareness by discussing interesting recent research directions.

翻译：移动机器人高效安全执行复杂任务的能力受限于其对环境——即态势的认知。高级推理、决策与执行能力使智能体能够在未知环境中自主行动。态势感知（SA）是人类的基本能力，已在心理学、军事、航空航天和教育等多个领域得到深入研究。然而，这一概念尚未在机器人学中得到充分考量，该领域当前聚焦于传感、空间感知、传感器融合、状态估计及同时定位与地图构建（SLAM）等单一概念化方向。因此，本研究旨在连接现有广泛的多学科知识，为建立我们认为对自主性至关重要的移动机器人完备态势感知系统铺平道路。为此，我们定义了构建机器人SA的主要组成部分及其职责领域。基于此，本文系统探究SA的每个方面，综述覆盖这些方面的前沿机器人算法，并讨论其当前局限性。值得注意的是，SA的关键要素仍不成熟，因现有算法发展仅将其性能局限于特定环境。然而，人工智能（AI）特别是深度学习（DL）已带来新方法，弥合了这些领域与实际场景部署之间的差距。此外，我们通过态势图（S-Graph）这一对经典场景图的泛化机制，发现连接高度碎片化的机器人理解算法空间的可能性。因此，我们最终通过讨论近期有趣的研究方向，勾勒出对机器人态势感知未来的愿景。