Recent Developments in Path Planning for Unmanned Ground Vehicles in Underground Mining Environment

This paper presents a thorough review of path-planning algorithms employed for the navigation of Unmanned Ground Vehicles (UGVs) in underground mining environments. It outlines the key components and requirements that are essential for an effective path planning framework, including sensors and the Robot Operating System (ROS). The review examines both global and local path-planning techniques, encompassing traditional graph-based methods, sampling-based approaches, nature-inspired algorithms and Reinforcement learning strategies. Through the analysis of the extant literatures on the subject, the review paper highlighted the strengths of the employed techniques, the application scenarios, the testing environments and the optimization strategies. The most favorable and relevant algorithms were identified. The paper acknowledges a significant limitation: the over-reliance on simulation testing for path-planning algorithms and the computational difficulties in implementing some of them in real mining condition. It concludes by emphasizing the necessity for full-scale research on path planning in real mining conditions.