###### Abstract  
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 \(\sim\)5-13% lower one-way delay in streaming data transfers, when compared to a simpler heuristic model. machine learning; continuous learning; multi-access networking; wireless networks access path selection; autonomous vehicles; River; MLFlow; RedsAI; ONNX; emulator +
Footnote †: journal: Medical Computing  
network connection emulation between virtual machines in real wired networks). Simulators simplify many architectural elements, which could alternatively be realized and tested in emulation environments. Thus, transitioning into a real operational mining environment should be easier after preliminary testing in emulation environments. Additionally, simulator-based approaches achieve a lower technology readiness (TRL) level (3), when compared to approaches including emulators (4-5). In this paper, we focused on an emulator-based approach for optimizing the routing and multi-access selection problem of moving vehicles in an underground mine. Particularly, a continuous machine learning (ML)-based approach was selected for facilitating access point selection in order to optimize one-way delay (OWD) between a moving vehicle and control center. A lower OWD should enable better quality of service of the products/services utilized at the control center for monitoring and controlling the mobile vehicles. Continuous ML refers [5] to the ability to periodically train and evaluate ML-based model(s) (based on the most recent data), which are updated for supporting decision making. For example, an incremental learning method and system for autonomous robot navigation was proposed [6]. Additionally, design of a best path selection algorithm, which can solve the problem of path planning for intelligent driving vehicles in the case of restricted driving, traffic congestions, and accidents, was presented [7]. Continuous ML-based decision making for multi-path radio access routing in an emulated mining environment was not solved, although ML-based decision making in multi-path routing was proposed [8; 9]. Development of ML-based models and applications to open radio access network (RAN) emulation environments were recently proposed, focusing on RAN scheduling and slicing [10]. Additionally, continuous ML solutions were proposed for model management (MLFlow) [11] and inference (RedisAI) [12]. MLFlow is a popular open-source platform for managing ML development, including experiment tracking, reproducibility, and deployment [11; 12; 13; 14]. A recent approach is to incrementally train models based on the most recent data (e.g., with River [13; 15; 16]). Finally, swarm intelligence [17] may be utilized to collectively learn solutions to several problems. However, the current solutions on ML-based decision making in multi-path routing of moving vehicles do not consider emulated mining environments, and do not enable continuous updating of ML-based models [8; 9] in such environments.