A framework for semantics-based situational awareness during mobile robot deployments

Area of Science:

• Robotics and Artificial Intelligence
• Human-Robot Interaction
• Disaster Response Technologies

Background:

• Human-robot teaming (HRT) is crucial for hazardous environments, requiring robust situational awareness (SA).
• Existing SA frameworks often overlook higher-level semantic understanding, critical for semi-autonomous systems.
• Effective HRT relies on both human operators and autonomous agents processing complex environmental information.

Purpose of the Study:

• To propose a generalizable framework for acquiring and integrating multi-modal semantic SA for mobile robots in remote deployments.
• To develop 'environment semantic indicators' and a 'Situational Semantic Richness' (SSR) metric for assessing environmental complexity.
• To enhance decision-making and operator attention in critical scenarios like search and rescue (SAR).

Main Methods:

• Developed a framework to combine multiple semantic information modalities for robot SA.
• Introduced 'environment semantic indicators' (e.g., risk, signs of human activity) for scene analysis.
• Proposed the 'Situational Semantic Richness' (SSR) metric to quantify overall environmental complexity.

Main Results:

• The proposed semantic indicators effectively captured diverse semantic information across different scenes.
• The SSR metric accurately reflected overall semantic changes, indicating information-rich situations.
• Experimental validation on a Jackal robot in a mock disaster environment confirmed framework sensitivity.

Conclusions:

• The developed framework enhances semantic SA for mobile robots in hazardous environments.
• The SSR metric provides a valuable tool for assessing situation complexity and guiding operator intervention.
• This approach improves the effectiveness of human-robot teaming in critical applications like disaster response.