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Updated: Dec 9, 2025

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A Study of Modified Infotaxis Algorithms in 2D and 3D Turbulent Environments.

Shurui Fan1, Dongxia Hao1, Xudong Sun1

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Summary

This study enhances the Infotaxis algorithm for hazardous gas source localization. Optimized reward functions improve search efficiency in both 2D and 3D environments, ensuring safer emergency responses.

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Area of Science:

  • Environmental Science
  • Robotics
  • Sensor Technology

Background:

  • Hazardous gas detection is crucial for environmental monitoring and emergency response.
  • Autonomous search algorithms are vital for locating gas emission sources safely, minimizing human exposure.
  • The Infotaxis algorithm offers a gradient-free approach for source tracking in turbulent environments.

Purpose of the Study:

  • To optimize the Infotaxis algorithm's reward function by balancing exploitation and exploration.
  • To propose an improved mobile strategy for hazardous gas source localization in 3D environments.
  • To enhance the efficiency and safety of emergency responses in hazardous gas scenarios.

Main Methods:

  • Modification of the Infotaxis algorithm's reward function to address exploitation-exploration imbalance.
  • Development of a novel mobile strategy tailored for three-dimensional search environments.
  • Comparative analysis of search task efficiency using average steps in 2D and 3D simulations.

Main Results:

  • The proposed Infotaxis algorithm optimization demonstrates a superior balance between exploitation and exploration.
  • The enhanced algorithm and mobile strategy show improved search performance in both 2D and 3D scenarios.
  • The optimized reward function leads to more efficient hazardous gas source localization compared to the standard Infotaxis algorithm.

Conclusions:

  • The optimized Infotaxis algorithm provides a more effective method for autonomous hazardous gas source localization.
  • The proposed strategy enhances search efficiency and safety in environmental monitoring and emergency response.
  • Balancing exploitation and exploration in the reward function is key to improving autonomous search performance.