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Supported Influence Mapping for Mobile Robot Pathfinding in Dynamic Indoor Environments.

Paweł Stawarz1, Dominik Ozog1, Wojciech Łabuński2

  • 1Department of Computer and Control Engineering, Faculty of Electrical and Computer Engineering, Rzeszów University of Technology, 35-959 Rzeszow, Poland.

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Summary
This summary is machine-generated.

This study explores using influence mapping for real-world indoor pathfinding. Combining this video game algorithm with sensor data shows promise for dynamic environments.

Keywords:
artificial intelligencedecision support systemsinfluence mappingmobile robots

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Area of Science:

  • Robotics and Artificial Intelligence
  • Computational Geometry

Background:

  • Pathfinding identifies optimal routes based on criteria like time or distance.
  • Dynamic environments, where topology changes in real-time, present significant pathfinding challenges.
  • Influence mapping, a technique from video games, has had limited success in real-world applications.

Purpose of the Study:

  • To evaluate the efficacy of influence mapping for real-time pathfinding in indoor environments.
  • To determine if combining influence mapping with remote sensor data improves performance in dynamic settings.

Main Methods:

  • The study adapted the influence mapping algorithm for practical indoor navigation.
  • Real-time data from remote sensors was integrated to update the environmental topology.
  • The algorithm's pathfinding performance was assessed in simulated dynamic indoor scenarios.

Main Results:

  • Influence mapping, when augmented with sensor data, demonstrated potential for dynamic indoor pathfinding.
  • The integration of real-time environmental information improved the algorithm's adaptability.
  • Initial results suggest feasibility but require further validation in diverse real-world conditions.

Conclusions:

  • Influence mapping combined with remote sensing offers a viable approach for dynamic indoor pathfinding.
  • This hybrid method shows promise for applications requiring real-time navigation in changing environments.
  • Further research is needed to optimize the algorithm and validate its robustness across various indoor settings.