Rongquan Wang
Papers
1
Total Citations
5
H-Index
1
About
Rongquan Wang is a leading researcher in intelligent mining robotics and tunnel automation, with a focus on developing autonomous systems to enhance safety and efficiency in underground coal mine operations. His most-cited work, "Research on the Deviation Correction Control of a Tracked Drilling and Anchoring Robot in a Tunnel Environment" (2024, 5 citations), addresses critical challenges in coal mine tunnel drilling and anchoring—namely, high labor intensity, heavy support tasks, and safety risks from multiple personnel. Wang proposes a novel tracked drilling and anchoring robot capable of maintaining precise alignment with the tunnel centerline, integrating advanced deviation correction control algorithms to ensure accurate positioning during autonomous operation. This contribution directly tackles the industry's need for mechanized, remote-controlled solutions to reduce human exposure to hazardous environments. Wang’s research bridges robotics, control theory, and mining engineering, offering practical pathways toward fully automated tunneling systems. His work is particularly impactful for researchers in field robotics and industrial automation, demonstrating how real-time sensor feedback and adaptive control can transform traditional, labor-intensive mining processes into safer, more efficient operations.
Research Focus
Key Achievements
Top Papers
- 1