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

Tianshu Ruan1, Aniketh Ramesh1, Hao Wang1

  • 1Extreme Robotics Lab (ERL) and National Center for Nuclear Robotics (NCNR), University of Birmingham, Birmingham, United Kingdom.

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Summary
This summary is machine-generated.

This study introduces a framework for robots to understand complex environments during hazardous missions. It uses semantic indicators and a Situational Semantic Richness metric to enhance human-robot teaming and situational awareness for better decision-making.

Keywords:
disaster-response roboticshuman–robot teamingsearch and rescue roboticssemantic understandingsemanticssituational awareness

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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.