Research Article
Research Progress on Path Planning Algorithms for Unmanned Ground Vehicles
@INPROCEEDINGS{10.4108/eai.24-4-2026.2364943, author={Wentao Liu}, title={Research Progress on Path Planning Algorithms for Unmanned Ground Vehicles}, proceedings={Proceedings of the 3rd International Conference on Mechanics, Electronics Engineering and Automation, ICMEEA 2026, April 24-26, 2026, Singapore, Singapore}, publisher={EAI}, proceedings_a={ICMEEA}, year={2026}, month={9}, keywords={Unmanned ground vehicles(UGV) Path planning Unstructured terrain Deep reinforcement learning(DRL)}, doi={10.4108/eai.24-4-2026.2364943} }- Wentao Liu
Year: 2026
Research Progress on Path Planning Algorithms for Unmanned Ground Vehicles
ICMEEA
EAI
DOI: 10.4108/eai.24-4-2026.2364943
Abstract
This paper systematically reviews the research progress of UGV off-road path planning algorithms in the past five years, classifying them into three categories: traditional search and physical model improvement algorithms, perception-based and geometric sampling planning algorithms, and deep reinforcement learning algorithms. Moreover, the existing algorithms have received more research attention in terms of search strategies and UGV travel energy consumption prediction.Studies have shown that although traditional physical algorithms have advantages in interpretability, DRL algorithms perform well in adaptability. However, the robustness of existing methods in extreme environments still needs to be improved. Finally, this paper looks forward to the future development direction of algorithms, pointing out that the integration of physical models for high-dimensional terrain construction, etc. will be the key to autonomous off-road navigation.
