矿山作业机器人具身智能与人机协同技术现状OA

Traditional teleoperation of mining robots faces significant bottlenecks in perception integrity,decision-making efficiency,and system generalization within complex operational scenarios.This study aims to explore the technical trajectory of mining robots as they evolve from basic operational autonomy to high-level cognitive intelligence.From the perspective of intelligent evolution,the development of control and decision-making technologies for mining robots is systematically reviewed.A three-stage progressive technical framework is proposed:achieving action-level autonomy through embodied intelligence,ensuring operational safety in complex conditions via Human-Robot Collaboration(HRC)and digital twins,and advancing cognitive decision-making through generative artificial intelligence.The study specifically analyzes the impact of Large Language Models(LLMs)on the reconstruction of HRC patterns and decision-making paradigms.Furthermore,the knowledge transfer mechanisms and application prospects of LLMs in highly uncertain mining environments are discussed.The analysis demonstrates that an LLM-empowered cyber-physical fusion architecture can effectively reduce human cognitive load and enhance system resilience.Finally,aligning with the"human-centered"philosophy of Industry 5.0,this paper envisions the future evolution of smart mines under the premise of safety and controllability.