Intelligent Robots and Autonomous Vehicle in Mining Engineering
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".
Deadline for manuscript submissions: 20 December 2026 | Viewed by 4
Special Issue Editor
Interests: mining robots; intelligent equipment; unmanned mining
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid advancement of intelligent technologies is transforming traditional mining engineering toward automation, digitalization, and unmanned operation. In complex and harsh mining environments, intelligent robots and autonomous vehicles have become essential for improving operational safety, efficiency, and sustainability. However, significant challenges remain, including dynamic working conditions, strong environmental interference, limited fault data, and the need for robust perception and decision-making capabilities.
This Special Issue aims to provide a comprehensive platform for recent advances in intelligent robots and autonomous vehicles applied to mining engineering. It focuses on emerging methodologies such as artificial intelligence, multi-sensor data fusion, autonomous perception, and intelligent control, as well as their integration into real-world mining systems. Topics of interest include, but are not limited to, fault diagnosis and health monitoring of mining equipment, autonomous navigation and path planning, cooperative multi-robot systems, digital twin technologies, and adaptive learning under complex environments.
By bringing together contributions from academia and industry, this Special Issue seeks to promote innovative solutions that bridge the gap between theoretical research and practical deployment. It aims to support the development of next-generation intelligent mining systems with enhanced reliability, adaptability, and efficiency.
Dr. Jianjian Yang
Guest Editor
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Keywords
- intelligent robots
- autonomous mining vehicles
- mining engineering
- fault diagnosis
- multi-sensor fusion
- autonomous navigation
- deep learning
- digital twin
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