Abstract: The technology of unmanned autonomous excavators focuses on four core areas: environmental perception, task decision-making, trajectory planning, and motion control. This technology integrates advanced sensor systems, intelligent algorithms, and autonomous control strategies to achieve efficient operation of excavators under unmanned operation. In terms of environmental perception, unmanned autonomous excavators adopt multimodal sensor fusion technology, which achieves high-precision recognition and understanding of complex work scenes through various sensors such as laser radar and visual cameras, and provides reliable data support for their subsequent decision-making and planning. In terms of task decision-making, unmanned autonomous excavators combine deep learning and reinforcement learning methods to process large amounts of information in real time, ensuring reliable and safe operations in complex construction tasks. By constructing a dynamic path planning algorithm and combining it with a digital twin platform for mining trajectory simulation verification, excavators can formulate optimal operation strategies and continuously optimize them during the execution process. Trajectory planning is another key area of unmanned autonomous excavator technology. By adopting dynamic path optimization algorithms, excavators can ensure high precision and safety of excavation actions, and achieve smooth and efficient operation processes. The unmanned autonomous excavator adopts advanced algorithms such as model predictive control, combined with real-time perception and optimization technology, to achieve high-precision trajectory tracking and stable operation. The latest progress of unmanned autonomous mining technology in these key areas is comprehensively reviewed, the current challenges and development trends are analyzed, and core insights and future prospects are extracted based on research results.