The Latest Research Trends and Application Prospects of Artificial Intelligence Path Planning Technology

DOI

10.2991/978-94-6463-823-3_50How to use a DOI?

Keywords

Path planning; AI; arithmetic optimization; Robotics and Autonomous Systems

Abstract

Path planning has demonstrated significant value across multiple fields as the core technology of intelligent navigation of autonomous systems. This review begins by examining the current development status of path planning technology, analyzing algorithmic innovations over recent years, and examining their performance in practical applications. Traditional path planning primarily relies on geometric optimization methods. However, with advancements in emerging technologies such as deep reinforcement learning (DRL: A methodology that employs deep neural networks to enable agents to learn optimal policies through interactions with an environment, aiming to maximize cumulative rewards.) and Neural Radiant Fields (NeRF A technique that leverages neural networks to reconstruct 3D scenes from sparse input views and render photorealistic images from novel viewpoints.), path planning is evolving towards advanced environmental semantic comprehension. This paper systematically reviews major contemporary path-planning algorithms, evaluating their adaptability, performance, and real-world application cases. It also explores existing challenges and future research directions. This study endeavors to establish an integrative analytical framework through a detailed discussion of dynamic environment adaptability, navigation in complex high-dimensional space, and enterprise-level applications, along with a comparative analysis of technologies and future trends. This framework seeks to clarify the evolutionary trajectory of autonomous path planning methodologies within robotic systems, emphasizing technical innovations like the integration of DRL for adaptive decision-making and NeRF for enhanced environmental perception. Despite these advances, limitations such as high computational requirements persist, suggesting future research should focus on developing lightweight algorithms for dynamic path planning and optimizing NeRF models for real-time robotic navigation.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Junye Wu
PY  - 2025
DA  - 2025/08/31
TI  - The Latest Research Trends and Application Prospects of Artificial Intelligence Path Planning Technology
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 500
EP  - 511
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_50
DO  - 10.2991/978-94-6463-823-3_50
ID  - Wu2025
ER  -