Human Failure Analysis and Fault-Tolerant Interaction Design of Deep-Sea Exploration Robots

Human-induced failures in deep-sea exploration robots are a key factor affecting the safety and reliability of deep-sea operations. This study systematically analyzes the mechanisms and influencing factors of human-induced failures in the extreme deep-sea environment and proposes a multi-layered fault-tolerant interaction design approach. By constructing a three-dimensional human-induced failure classification model, the authors reveal the coupled relationship between environmental pressure, task complexity, and operator cognitive characteristics. The developed layered fault-tolerant architecture provides comprehensive protection, from hardware redundancy to cognitive collaboration. Experimental results show that it can reduce the incidence of typical human-induced failures by 65%-78%. The study innovatively proposes the concept of "predictive fault tolerance," combining digital twins with operational model learning to enable the system to anticipate risks. Dynamic interface optimization and multimodal feedback strategies significantly enhance the operator's situational awareness. The research findings provide a systematic theoretical framework and practical guidance for the design of deep-sea exploration robots, promoting a paradigm shift from a "machine-centric" approach to a "human-machine collaborative" approach, and are of significant significance for improving the safety and efficiency of deep-sea operations.