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AI-Enhanced Thermal-Visual-Inertial Odometry and Autonomous Planning for GPS-Denied Search-and- Rescue Robotics.

Islam T Almalkawi1, Sabya Shtaiwi1, Alaa Alhowaide2

  • 1Computer Engineering Department, Faculty of Engineering, The Hashemite University, Zarqa 13133, Jordan.

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Summary

This study introduces an autonomous robot for search and rescue (SAR) in GPS-denied environments. The robot uses fused sensor data for navigation and survivor detection, enabling successful missions without external beacons.

Keywords:
GPS-denied environmentsautonomous navigationdeep reinforcement learningmulti-sensor fusionsearch and rescue robots

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Sensor Fusion

Background:

  • Global Positioning System (GPS) unavailability poses significant challenges for autonomous navigation in search and rescue (SAR) missions within collapsed or underground environments.
  • Existing systems often struggle with sensor degradation due to environmental factors like smoke, dust, and dynamic clutter, limiting their effectiveness.

Purpose of the Study:

  • To develop and evaluate an autonomous ground robot capable of performing SAR missions in GPS-denied environments.
  • To integrate low-cost, multimodal sensor inputs into a unified, computation-efficient architecture for robust localization and navigation.

Main Methods:

  • The system integrates Thermal-Visual Odometry (TV-VO) with Zero-Velocity Updates (ZUPT) for drift-resistant localization.
  • RescueGraph was employed for multimodal survivor detection, achieving an F1-score of 0.6923 and an AUC of 0.976.
  • A Proximal Policy Optimization (PPO) planner was utilized for adaptive navigation under uncertainty.

Main Results:

  • The integrated navigation stack demonstrated embedded-feasible runtime behavior and supported return-to-base functionality without external beacons.
  • TV-VO+ZUPT effectively reduced localization drift in internal evaluations.
  • The system achieved full mission completion in SAR-style trials, with PPO showing advantages in navigation adaptability compared to A*.

Conclusions:

  • The proposed low-cost sensor-fusion and learning-assisted navigation framework shows practical promise for GPS-denied SAR robotics.
  • The unified architecture and multimodal sensor integration enable robust performance in challenging, unstructured environments.
  • Further development of such systems can significantly enhance the capabilities of autonomous robots in critical rescue operations.