@inproceedings{d3c9049e7cb04155a8908642421cef0a,
title = "Intelligent Path Planning for Off-Road Navigation: A Q Learning Approach with Terrain and Distance Optimization",
abstract = "In off-road scenarios, the combined impact of obstacles and terrain poses a significant challenge to vehicle navigation. Currently, many studies focus on static environments, and these methods fail to adapt to dynamic environments due to their neglect of moving obstacles. Therefore, based on the Q Learning algorithm, this paper proposes a method for initializing the Q-table using a global route and designs elevation difference and distance reward functions to enable effective path planning in low-dynamic off-road environments. Experimental results demonstrate that compared to an untuned Q Learning algorithm, this method converges faster and produces smoother routes, thereby better adapting to dynamic off-road environments.",
keywords = "Dynamic Path Planning, Off-Road Environment, Q Learning",
author = "Duohang Sun and Chenxi Wang and Chengxu Zhou and Xiaoqing Liu and Haitao Xiao",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.; 24th International Symposium on Knowledge and Systems Sciences, KSS 2025 ; Conference date: 28-11-2025 Through 01-12-2025",
year = "2026",
doi = "10.1007/978-981-95-4990-0\_23",
language = "英语",
isbn = "9789819549894",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "324--336",
editor = "Xijin Tang and Haoxiang Xia and Xianneng Li and Huynh, \{Van Nam\}",
booktitle = "Knowledge and Systems Sciences - 24th International Symposium, KSS 2025, Proceedings",
}