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

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Featured Application: System and method for continuously improving or adapting multi-access router path selection of autonomous moving vehicles in changing environments. 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.

Original languageEnglish
Article number10679
Number of pages23
JournalApplied Sciences (Switzerland)
Volume12
Issue number20
DOIs
Publication statusPublished - Oct 2022
MoE publication typeA1 Journal article-refereed

Keywords

  • autonomous vehicles
  • continuous learning
  • emulator
  • machine learning
  • MLFlow
  • multi-access networking
  • ONNX
  • RedisAI
  • River
  • wireless networks access path selection

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