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A Dual-Layer Hybrid-A* Path Planning Algorithm for Unstructured Environments Based on Phase Windows.

Tianxiao Zhu1, Ziyu Xu1, Rujiang Zhu1

  • 1School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China.

Sensors (Basel, Switzerland)
|January 10, 2026
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Summary
This summary is machine-generated.

This study introduces a dual-layer Hybrid-A* algorithm for mobile robot path planning. It improves stability and safety on complex terrain by adapting to dynamic environments and robot constraints.

Keywords:
Hybrid-A* algorithmmobile robotpath planningquadratic programmingunstructured environments

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Science

Background:

  • Mobile robots require robust path planning for autonomous navigation.
  • Complex terrain poses challenges to robot stability and safety.
  • Existing algorithms struggle with dynamic terrain and robot motion constraints.

Purpose of the Study:

  • To propose a novel dual-layer Hybrid-A* algorithm for enhanced path planning.
  • To improve robot stability and safety in unstructured environments.
  • To address limitations of traditional path-planning methods.

Main Methods:

  • Developed a dual-layer Hybrid-A* algorithm with dynamic phase windows.
  • Integrated real-time feedback and adaptive adjustments for path refinement.
  • Employed bicubic interpolation for elevation map refinement and posture estimation.
  • Utilized dynamic cost functions, adaptive expansion radius, pruning, and phase-window activation.

Main Results:

  • The proposed algorithm outperforms traditional methods in complex terrain.
  • Generated optimized paths that satisfy robot motion constraints.
  • Demonstrated improved efficiency and safety in real-world applications.

Conclusions:

  • The dual-layer Hybrid-A* algorithm offers a significant advancement in mobile robot path planning.
  • The approach enhances robot navigation capabilities in challenging environments.
  • This method ensures both efficiency and safety for autonomous systems.