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地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（Situational Awareness, 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，机器人领域尚未充分关注这一概念，该领域长期聚焦于感知、空间认知、传感器融合、状态估计和同步定位与地图构建（SLAM）等单一孤立的概念。因此，本研究旨在整合广泛的多学科已有知识，以奠定移动机器人完整态势感知系统的基础——我们认为这是实现自主性的关键。为此，我们定义了构建机器人态势感知的主要组成部分及其能力范围。据此，本文对态势感知的每个方面进行了研究，调研了覆盖这些方面的前沿机器人算法，并讨论了其当前局限性。值得注意的是，由于当前算法发展仅能适应特定环境，态势感知的关键方面仍不成熟。然而，人工智能（AI），特别是深度学习（DL），提供了弥合这些领域与现实场景部署之间鸿沟的新方法。此外，我们发现了通过态势图（Situational Graph, S-Graph）——对经典场景图（Scene Graph）的泛化——这一机制来链接高度碎片化的机器人理解算法空间的机遇。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构想。