Author(s)

Xiankun Lin ¹, Linsen Liang ¹

Affiliation(s)

¹ School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, China

Corresponding Author

Xiankun Lin

ABSTRACT

With the rapid advancement of artificial intelligence technology, robot path planning has shifted from traditional algorithm-driven approaches to data and model-driven paradigms. To address common challenges in university-level Robot Path Planning courses—such as overly abstract theoretical concepts, difficulties in programming implementation, and outdated algorithmic frameworks—this study proposes an AI-assisted teaching model incorporating a large language model (LLM) platform. Through a tiered curriculum architecture, a hybrid virtual-physical laboratory environment, and project-based instructional strategies, the proposed model seeks to equip students with proficiency in applying state-of-the-art techniques—from classical algorithms to deep reinforcement learning—to path planning challenges, fostering interdisciplinary mindsets and hands-on engineering competencies.

KEYWORDS

Artificial Intelligence; Robotics; Path Planning; Teaching Reform; Reinforcement Learning; ROS

CITE THIS PAPER

Xiankun Lin, Linsen Liang. Teaching Reform and Practical Exploration of Robot Path Planning Programming Based on the Artificial Large Language Platform. Advances in Vocational and Technical Education (2026). Vol. 8, No.1, 74-81. DOI: http://dx.doi.org/10.23977/avte.2026.080110.

REFERENCES

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Sponsors, Associates, and Links

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• Advances in Educational Technology and Psychology
  
  
• Curriculum and Teaching Methodology
  
  
• Transactions on Comparative Education
  
  
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