Interdisciplinary Center for Security Reliability and Trust (SnT), University of Luxembourg, 1855 Luxembourg, Luxembourg.
Department of Engineering, Faculty of Science, Technology, and Medicine (FSTM), University of Luxembourg, 1359 Luxembourg, Luxembourg.
Sensors (Basel). 2023 May 17;23(10):4849. doi: 10.3390/s23104849.
The capability of a mobile robot to efficiently and safely perform complex missions is limited by its knowledge of the environment, namely the . 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 , 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.
移动机器人执行复杂任务的能力受到其对环境的了解程度的限制，即知识。高级推理、决策和执行技能使智能体能够在未知环境中自主行动。态势感知 (SA) 是人类的一项基本能力，已在心理学、军事、航空航天和教育等各个领域进行了深入研究。然而，机器人学尚未考虑到这一点，机器人学专注于单一的概念，如感知、空间感知、传感器融合、状态估计和同时定位与地图构建 (SLAM)。因此，本研究旨在将广泛的多学科现有知识联系起来，为移动机器人建立完整的态势感知系统铺平道路，我们认为这对于自主性至关重要。为此，我们定义了构建机器人态势感知的主要组件及其能力领域。因此，本文调查了 SA 的各个方面，调查了涵盖它们的最先进的机器人算法，并讨论了它们当前的局限性。值得注意的是，SA 的基本方面仍然不成熟，因为当前算法的发展将其性能限制在特定的环境中。然而，人工智能 (AI)，特别是深度学习 (DL)，为弥合这些领域之间的差距带来了新的方法，这些差距将它们与现实世界的场景隔离开来。此外，通过机制发现了将机器人理解算法的广阔而分散的空间连接起来的机会，该机制是广为人知的场景图的推广。因此，我们通过讨论最近有趣的研究方向来塑造我们对机器人态势感知未来的愿景。
