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An Enhanced A*-DWA Fusion Algorithm for Robot Navigation in Complex Environments.

Huifang Bao1,2, Jie Fang1,2, Mingxing Fang3

  • 1School of Electrical and Photoelectronic Engineering, West Anhui University, Lu'an 237012, China.

Biomimetics (Basel, Switzerland)
|February 26, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a fusion planning framework for autonomous mobile robots, enhancing navigation in complex environments. The new approach improves path length, smoothness, and safety for efficient and stable robot operation.

Keywords:
DWAcomplex environmentenhanced A*fusion planningmobile robot

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Area of Science:

  • Robotics and Artificial Intelligence
  • Bio-inspired Navigation Systems

Background:

  • Autonomous mobile robots face navigation challenges in dynamic and complex environments.
  • Existing path planning algorithms often struggle with real-time obstacle avoidance and efficiency.

Purpose of the Study:

  • To develop a novel fusion planning framework integrating enhanced A* and improved Dynamic Window Approach (DWA).
  • To mimic biological navigation mechanisms for global path planning and local obstacle avoidance.

Main Methods:

  • The study optimizes the A* algorithm's global path through a three-stage strategy (redundant node removal, path relaxation).
  • An enhanced DWA incorporates dynamic obstacle perception and adaptive weight adjustment for intelligent local planning.
  • Simulations on various grid maps and physical verification on a LiDAR-equipped mobile robot were conducted.

Main Results:

  • The proposed algorithm demonstrated superior performance over conventional methods in path length, smoothness, and safety.
  • Physical verification confirmed stable static path tracking and real-time obstacle avoidance in indoor environments.
  • The fusion framework provides a robust solution for autonomous navigation.

Conclusions:

  • The bio-inspired fusion planning framework offers a viable and efficient solution for autonomous mobile robot navigation.
  • This approach enhances robot safety and stability in unpredictable and complex environments.