This paper describes an autonomous loading system for load-haul-dump (LHD) machines used in underground mining. The loading of fragmented rocks from draw points is a complex task due to many factors including: bucket-rock interaction forces that are difficult to model, humidity that increases cohesion forces, and the possible presence of boulders. The proposed system is designed to integrate all the relevant tasks required for ore loading: rock pile identification, LHD positioning in front of the ore pile, charging and excavating into the ore pile, pull back and payload weighing. The system follows the shared autonomy paradigm: given that the loading process may not be completed autonomously in some cases, it takes into account that the machine/agent can detect this situation and ask a human operator for assistance. The most novel component of the proposed autonomous loading system is the excavation algorithm, and the disclosure of the results obtained from its application in a real underground production environment. The excavation method is based on the way that human operators excavate: while excavating, the bucket is tilted intermittently in order to penetrate the material, and the boom of the LHD is lifted on demand to prevent or correct wheel skidding. Wheel skidding is detected with a patented method that uses LIDAR-based odometry and internal measurements of the LHD. While a complete loading system was designed, the validation had to be divided in two stages. One stage included the rock pile identification and positioning, and the other included the charging, excavation, pull back, and weighting processes. The stage concerning the excavation algorithm was validated using full-scale experiments with a real-size LHD in an underground copper mine in the north of Chile, while the stage concerning the rock pile identification was later validated using real data. The tests showed that the excavation algorithm is able to load the material with an average of 90% bucket fill factor using between three and four attempts (professional human operators required between two and three loading attempts in this mine).

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Battery energy systems are currently one of the most common power sources found in mobile electromechanical devices. In all these equipment, assuring the autonomy of the system requires to determine the battery state-of-charge (SOC) and predicting the end-of-discharge time with a high degree of accuracy. In this regard, this paper presents a comparative analysis of two well-known Bayesian estimation algorithms (Particle filter and Unscented Kalman filter) when used in combination with Outer Feedback Correction Loops (OFCLs) to estimate the SOC and prognosticate the discharge time of lithium-ion batteries. Results show that, on the one hand, a PF-based estimation and prognosis scheme is the method of choice if the model for the dynamic system is inexact to some extent; providing reasonable results regardless if used with or without OFCLs. On the other hand, if a reliable model for the dynamic system is available, a combination of an Unscented Kalman Filter (UKF) with OFCLs outperforms a scheme that combines PF and OFCLs.

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