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Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology.

Xiao Ji1, Peng Liu1, Meng Zhang1

  • 1China Satellite Network Digital Technology Co., Ltd., Xiong'an 070001, China.

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Summary
This summary is machine-generated.

This study introduces a hybrid path planning framework using A* search and Deep Q-Network (DQN) optimization for complex routing. The novel approach enhances terrestrial routing by integrating diverse geospatial data for improved risk avoidance and reduced travel times.

Keywords:
A* algorithm optimizationdeep reinforcement learningdynamic decision makingmulti-source datapath planning algorithms

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Area of Science:

  • * Geographic Information Science
  • * Artificial Intelligence
  • * Robotics and Autonomous Systems

Background:

  • * Complex terrestrial routing presents significant challenges for autonomous systems and logistics.
  • * Existing path planning methods often struggle with dynamic environments and multi-attribute optimization.
  • * Integration of diverse geospatial data (terrain, traffic, road networks) is crucial for robust planning.

Purpose of the Study:

  • * To develop a hybrid path planning framework integrating global A* search with local Deep Q-Network (DQN) optimization.
  • * To fuse ASTER GDEM terrain data and OpenStreetMap (OSM) road networks for a comprehensive geospatial database.
  • * To enhance adaptability to dynamic environments and improve performance in risk avoidance and travel time reduction.

Main Methods:

  • * Development of a dynamic-heuristic A* algorithm incorporating traffic signals and congestion penalties.
  • * Implementation of a Deep Q-Network (DQN)-based local decision module for enhanced environmental adaptability.
  • * Construction of a standardized geospatial database integrating elevation, traffic, and road attributes from ASTER GDEM and OSM.

Main Results:

  • * The proposed hybrid framework demonstrated superior performance in risk avoidance compared to traditional models.
  • * Significant reduction in travel time was achieved through the integration of dynamic heuristics and local optimization.
  • * Enhanced capability in handling dynamic obstacles and complex urban routing scenarios was validated.

Conclusions:

  • * The unified architecture enhances path planning robustness for terrestrial routing challenges.
  • * The hybrid approach provides a foundation for real-time applications in emergency response and smart logistics.
  • * Integration of multi-source data and AI-driven optimization offers a powerful solution for complex navigation problems.