Concept and Architecture for Applying Continuous Machine Learning in Multi-Access Routing at Underground Mining Vehicles

• Published in: Applied sciences Vol. 12; no. 20; p. 10679
• Main Authors: Pääkkönen, Pekka, Backman, Jere, Pakkala, Daniel, Paananen, Jori, Seppänen, Kari, Ahola, Kimmo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2022
• Subjects: Architectural elements; Architecture; Autonomous vehicles; Changing environments; continuous learning; Control centres; Decision making; Design; Environmental changes; Environmental monitoring; Learning algorithms; Machine learning; Mining; multi-access networking; Remote control; Remote monitoring; Remote sensors; Research methodology; River; Route planning; Route selection; Underground mines; Underground mining; Vehicles; Wireless communications; Wireless networks; wireless networks access path selection
• DOI: 10.3390/app122010679
• ISSN: 2076-3417

Autonomous moving vehicles facilitate mining of ore in underground mines. The vehicles are usually equipped with many sensor-based devices (e.g., Lidar, video camera, proximity sensor, etc.), which enable environmental monitoring, and remote control of the vehicles at the control center. Transfer of sensor-based data from the vehicles towards the control center is challenging due to limited connectivity enabled by the multi-access technologies of the communication infrastructure (e.g., 5G, Wi-Fi) within the underground mine, and the mobility of the vehicles. This paper presents design, development, and evaluation of a concept and architecture enabling continuous machine learning (ML) for optimizing route selection of real-time streaming data in a real and emulated underground mining environment. Continuous ML refers to training and inference based on the most recently available data. Experiments in the emulator indicated that utilization of a ML-based model (based on the RandomForestRegressor) in decision making achieved ~5–13% lower one-way delay in streaming data transfers, when compared to a simpler heuristic model.