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An Eight-Direction Scanning Detection Algorithm for the Mapping Robot Pathfinding in Unknown Indoor Environment.

Le Jiang1,2, Pengcheng Zhao3, Wei Dong4

  • 1School of Remote Sensing & Information Engineering, Wuhan University, Wuhan 430079, China. jiangl@whu.edu.cn.

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Summary
This summary is machine-generated.

A new eight-direction scanning detection (eDSD) algorithm enhances mobile robot navigation in unknown indoor environments. This pathfinding method ensures efficient and safe traversal by analyzing probabilistic path vectors for optimal local and global path planning.

Keywords:
autonomous obstacle avoidanceeight-direction scanning detectionmapping robotpathfindingunknown indoor environment

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Science

Background:

  • Mobile robots require efficient and safe navigation in unknown indoor environments.
  • Simultaneous Localization and Mapping (SLAM) is crucial for acquiring environmental data.
  • Existing pathfinding algorithms may not adequately address the complexities of unknown spaces.

Purpose of the Study:

  • To propose a novel pathfinding algorithm, the eight-direction scanning detection (eDSD), for mobile robots.
  • To enable efficient and safe navigation and mapping in unknown indoor environments.
  • To develop both local and global pathfinding capabilities for enhanced traversal.

Main Methods:

  • Utilized a laser-based SLAM algorithm for simultaneous localization and mapping.
  • Developed the eight-direction scanning detection (eDSD) algorithm analyzing probabilistic path vectors.
  • Integrated local pathfinding for immediate obstacle avoidance and optimal path selection.
  • Incorporated global pathfinding to reduce repeated traversals in large, complex environments.

Main Results:

  • The eDSD algorithm successfully calculated probabilistic path vectors for optimal local path selection.
  • Field experiments in diverse indoor environments showed minimal deviation from ideal paths in length and time.
  • The algorithm demonstrated efficiency in finding optimal local paths rapidly.

Conclusions:

  • The proposed eDSD algorithm is highly adaptable and practical for mobile robot navigation in various indoor settings.
  • The combination of local and global pathfinding effectively addresses challenges in unknown and complex environments.
  • The eDSD algorithm provides a robust solution for efficient and safe robot traversal and mapping.