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Path Planning of a Mobile Delivery Robot Operating in a Multi-Story Building Based on a Predefined Navigation Tree.

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This study introduces a novel path planning method for mobile robots in multi-story buildings, utilizing a navigation tree and Dijkstra's algorithm for efficient package delivery. The approach ensures effective autonomous robot routing within complex indoor environments.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Science

Background:

  • Multi-story buildings present unique challenges for mobile robot navigation due to vertical transportation needs (elevators).
  • Efficient path planning is crucial for autonomous delivery robots to ensure timely and effective package transport.
  • Existing methods may not fully address the complexities of multi-floor navigation and elevator integration.

Purpose of the Study:

  • To develop and validate a robust path planning solution for mobile robots in multi-story buildings.
  • To integrate spatial and operational information, including elevator usage, into the navigation strategy.
  • To demonstrate the effectiveness of the proposed method through simulated routing results.

Main Methods:

  • Definition of a static navigation tree representing weighted paths across multiple floors.
  • Application of Dijkstra's algorithm to the navigation tree for optimal route planning.
  • Simulation of autonomous delivery robot operations in a multi-story building environment.

Main Results:

  • The proposed method successfully plans efficient routes for mobile robots in multi-story buildings.
  • Dijkstra's algorithm effectively estimates the total length of delivery routes.
  • Simulated results validate the effectiveness of the navigation tree approach for autonomous delivery robots.

Conclusions:

  • The developed path planning strategy provides an effective solution for mobile robot navigation in multi-story buildings.
  • The integration of a navigation tree and Dijkstra's algorithm offers a scalable approach for autonomous delivery systems.
  • This research contributes to the advancement of autonomous logistics within complex indoor environments.