Typical Method Combination of Global Path Planning Technology Based on SLAM Technology

Xinyue Li

doi:10.62051/68cdnf60

Authors

Xinyue Li

DOI:

https://doi.org/10.62051/68cdnf60

Keywords:

Global path planning; Reinforcement learning; Deep learning; Adaptability.

Abstract

As automation and intelligent technologies progress rapidly, robotics has seen broad adoption in multiple sectors such as autonomous transportation, UAV operations, home automation, and industrial manufacturing. In order to achieve autonomous navigation of robots in unknown, dynamic, and complex environments, global path planning technology has become a key research direction. This article explores the typical methods and optimization research of global path planning based on SLAM (Simultaneous Localization and Mapping) technology. A detailed analysis was conducted on the advantages, disadvantages, and applicable scenarios of A* algorithm, RRT algorithm, ant colony algorithm, and Dijkstra algorithm, and the challenges and limitations of these algorithms in practical applications were pointed out. In addition, by combining reinforcement learning and deep learning techniques, the article further explores the potential technical challenges faced by current path planning technologies, and proposes the development direction of future path planning technologies, including the intelligence, adaptability, and robustness improvement of algorithms in complex environments.

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