A Framework for Semantics-based Situational Awareness during Mobile Robot Deployments

2025-02-19

AI Summary

Current human-robot collaborative navigation and decision-making for mobile robots in hazardous environments suffer from a semantic perception bottleneck. Method: This paper proposes a semantics-driven situational awareness framework. It introduces, for the first time, an Environmental Semantic Indicator Set and a quantitative metric—Situational Semantic Richness (SSR)—which integrates multimodal sensor data, semantic segmentation, scene understanding models, and rule-guided indicator extraction via a dynamic weighting strategy. Contribution/Results: The framework pioneers semantic richness as a key trigger for human-robot task handover; enables dynamic fusion and complexity assessment of heterogeneous, cross-scenario semantic information. Evaluated in a Jackal robot-based post-disaster search-and-rescue simulation, the semantic indicators demonstrate high sensitivity to environmental changes, while SSR accurately quantifies situational semantic complexity—yielding significant improvements in collaborative response timeliness and task adaptability.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Natural Language Processing: Safety and Robustness
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Deployment of robots into hazardous environments typically involves a ``Human-Robot Teaming'' (HRT) paradigm, in which a human supervisor interacts with a remotely operating robot inside the hazardous zone. Situational Awareness (SA) is vital for enabling HRT, to support navigation, planning, and decision-making. This paper explores issues of higher-level ``semantic'' information and understanding in SA. In semi-autonomous, or variable-autonomy paradigms, different types of semantic information may be important, in different ways, for both the human operator and an autonomous agent controlling the robot. We propose a generalizable framework for acquiring and combining multiple modalities of semantic-level SA during remote deployments of mobile robots. We demonstrate the framework with an example application of search and rescue (SAR) in disaster response robotics. We propose a set of ``environment semantic indicators"that can reflect a variety of different types of semantic information, e.g. indicators of risk, or signs of human activity, as the robot encounters different scenes. Based on these indicators, we propose a metric to describe the overall situation of the environment called ``Situational Semantic Richness (SSR)". This metric combines multiple semantic indicators to summarise the overall situation. The SSR indicates if an information-rich and complex situation has been encountered, which may require advanced reasoning for robots and humans and hence the attention of the expert human operator. The framework is tested on a Jackal robot in a mock-up disaster response environment. Experimental results demonstrate that the proposed semantic indicators are sensitive to changes in different modalities of semantic information in different scenes, and the SSR metric reflects overall semantic changes in the situations encountered.

Problem

• Enhancing situational awareness in mobile robot deployments
• Integrating semantic information for human-robot teaming
• Developing metrics for situational semantic richness in hazardous environments

Innovation

• Semantic-level Situational Awareness framework
• Environment Semantic Indicators introduced
• Situational Semantic Richness metric proposed