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A task level fusion autonomous switching mechanism.

Bingyu Lv1,2, Xianchang Wang1,2,3, Rui Zhang1,2

  • 1College of Computer Science and Technology, Jilin University, Changchun, Jilin, China.

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This study introduces a novel fusion module to enhance indoor positioning accuracy for firefighting robots in the Fire Emergency Reaction Dispatching (FERD) system. The module improves robot navigation and decision-making in complex environments.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Sensor Fusion

Background:

  • Accurate indoor positioning is critical for autonomous firefighting robots.
  • Current Fire Emergency Reaction Dispatching (FERD) systems lack sufficient positioning accuracy in cluttered indoor environments.
  • Existing systems struggle with navigation and decision-making due to positioning limitations.

Purpose of the Study:

  • To develop a fusion module that enhances the positioning accuracy of unmanned vehicles within the FERD system.
  • To improve the reliability of autonomous decision-making for firefighting robots in challenging indoor scenarios.
  • To create a robust system for precise indoor localization.

Main Methods:

  • Proposed a Blackboard architecture-based fusion module for integrating multiple sensor data.
  • Implemented six key strategies: denoising, spatial alignment, confidence update, observation filtering, data fusion, and fusion decision.
  • Trained the module using multi-sensor data in an indoor, multi-obstacle environment, incorporating sensor error data and scenario parameters.

Main Results:

  • The fusion module demonstrated significant improvements in positioning accuracy for firefighting robots.
  • Experimental results confirmed the module's effectiveness in indoor environments with multiple obstacles.
  • The system successfully achieved globally optimal positioning results by integrating multi-level data.

Conclusions:

  • The developed fusion module effectively enhances indoor positioning accuracy for the FERD system.
  • This advancement supports more reliable autonomous decision-making and motion control for firefighting robots.
  • The research provides a robust solution for precise localization in complex indoor rescue scenarios.