Work

Dynamic Integration of Q-Learning and A-APF for Efficient Path Planning in Complex Underground Mining Environments

Year: 2025

Type: article

Abstract: To address low learning efficiency and inadequate path safety in spraying robot navigation within complex obstacle-rich environments—with dense, dynamic, unpredictable obstacles challenging conventional methods—this paper... | Find, read and cite all the research you need on Tech Science Press

Source: Computers, materials & continua/Computers, materials & continua (Print)

DOI: 10.32604/cmc.2025.071319

Authors: Chang Su, Liangliang Zhao, Dongbing Xiang

Institution: Anhui University of Science and Technology

Language: English

Topic: Robotic Path Planning Algorithms

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: National Natural Science Foundation of China, Anhui University, Anhui University of Science and Technology

Award: G933897665