In this article, the current state of the art in the area of autonomously working and mobile robots used for inspections in deep underground mining and exploration is described, and directions for future development are highlighted. The increasing demand for CRMs (critical raw materials) and deeper excavations pose a higher risk for people and require new solutions in the maintenance and inspection of both underground machines and excavations. Mitigation of risks and a reduction in accidents (fatal, serious and light) may be achieved by the implementation of mobile or partly autonomous solutions such as drones for exploration, robots for exploration or initial excavation, etc. This study examines various types of mobile unmanned robots such as ANYmal on legs, robots on a tracked chassis, or flying drones. The main scope of this review is the evaluation of the effectiveness and technological advancement in the aspect of improving safety and efficiency in deep underground and abandoned mines. Notable possibilities are multi-sensor systems or cooperative behaviors in systems which involve many robots. This study also highlights the challenges and difficulties of working and navigating (in an environment where we cannot use GNSS or GPS systems) in deep underground mines. Mobile inspection robots have a major role in transforming underground operations; nevertheless, there are still aspects that need to be developed. Further improvement might focus on increasing autonomy, improving sensor technology, and the integration of robots with existing mining infrastructure. This might lead to safer and more efficient extraction and the SmartMine of the future.

Remote drilling technologies play a crucial role in automating both underground and open-pit hard rock mining operations. These technologies enhance efficiency and, most importantly, improve safety in the mining sector. Autonomous drilling rigs can navigate to pre-determined positions and utilize the appropriate parameters to drill boreholes effectively. This article explores various aspects of automation, including the integration of advanced data collection methods that monitor the drilling parameters and facilitate the creation of 3D models of rock hardness. The shift toward machine automation involves transitioning from human-operated machines to systems powered by artificial intelligence, which are capable of making real-time decisions. Navigating underground environments presents unique challenges, as traditional RF-based localization systems often fail in these settings. New solutions, such as constant localization and mapping techniques like SLAM (simultaneous localization and mapping), provide innovative methods for navigating mines, particularly in uncharted territories. The development of robotic exploration rigs equipped with modules that can operate autonomously in hazardous areas has the potential to revolutionize mineral exploration in underground mines. This article also discusses solutions aimed at validating and improving existing methods by optimizing drilling strategies to ensure accuracy, enhance efficiency, and ensure safety. These topics are explored in the context of the Horizon Europe-funded PERSEPHONE project, which seeks to deliver fully autonomous, sensor-integrated robotic systems for deep mineral exploration in challenging underground environments.

Reliable radar inertial odometry (RIO) demands precise correction of inertial measurement unit (IMU) acceleration drift during the synchronization of heterogeneous sensors. This bias drift, most evident in acceleration measurements, worsens in subterranean environments due to extreme cold or gravitational components acting along the robot’s feed-forward axis. Left uncorrected, such drifts substantially degrade sensor fusion performance, leading to dead-reckoning, particularly when employing cost-effective platforms such as the Pixhawk IMU in combination with multiple mmWave radars. In addition, radar point clouds differ fundamentally from LiDAR data, being inherently sparse, noisy, and prone to flickering effects, which further complicates the challenge of achieving stable and reliable odometry. To address these challenges, this article presents a novel two-stage multi-radar inertial odometry (MRIO) framework for resilient localization and mapping in GPS-denied subterranean environments. In the first stage, radar ego-velocity estimation, formulated through a least-squares approach, is incorporated into an Extended Kalman Filter (EKF) for IMU bias correction. The resulting drift-free accelerations are then fused in the second stage with measurements from multiple radars and the IMU to refine odometry performance. Beyond odometry, the framework also supports radar-based mapping by leveraging the generated robot’s translational and angular displacement by the proposed framework. Starting with a range-based outlier filter, least-squares ego-velocity reconstruction, the MRIO framework enables robust ego-velocity estimation, localisation, and mapping using only radar and IMU measurements. The proposed framework is extensively validated across multiple experimental scenarios, including real-time deployment in smoke-filled subterranean environments. Comparative evaluations involving multiple FMCW radar setups, LiDAR inertial odometry (LIO), and prior EKF-RIO methods further validate the framework’s robustness and its consistent capability to achieve precise localization and mapping in challenging perceptual environments.