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LLM-DWA: a hybrid path planning framework combining large language models with the dynamic window approach.

Jeonghee Seo1, Eunsung Kim1, Andrew Jaeyong Choi2

  • 1School of Computing, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam, 13120, Republic of Korea.

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This study enhances the Dynamic Window Approach (DWA) for robot navigation by integrating Large Language Models (LLMs). The LLM-powered DWA improves path planning efficiency and reduces goal-reaching time, especially in complex environments with U-shaped obstacles.

Keywords:
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Area of Science:

  • Robotics
  • Artificial Intelligence

Background:

  • The Dynamic Window Approach (DWA) algorithm faces challenges with local minima and inefficient path planning in complex environments.
  • Conventional DWA lacks the ability to incorporate prior environmental knowledge, leading to degraded performance, particularly with U-shaped obstacles.

Purpose of the Study:

  • To improve the goal-reaching performance and efficiency of the DWA algorithm.
  • To address the local minima problem and reduce planning time in complex navigation scenarios.

Main Methods:

  • Integration of Large Language Models (LLMs) with the Dynamic Window Approach (DWA).
  • Utilizing LLMs' reasoning capabilities to interpret environmental information and generate intermediate waypoints.
  • Experimental validation in 2D grid environments and 3D simulation platforms.

Main Results:

  • The proposed LLM-based hybrid method significantly improves efficiency in U-shaped obstacle scenarios.
  • Shorter goal-reaching times were observed compared to the conventional DWA.
  • Demonstrated enhanced navigation performance in complex environments.

Conclusions:

  • Combining LLMs with DWA effectively overcomes limitations of the conventional approach.
  • LLM-enhanced DWA offers a promising solution for efficient and robust robot navigation in complex environments.
  • The hybrid method shows superior performance in scenarios with challenging obstacle configurations.