Robotic arms on space stations are essential for extravehicular operations and emergency rescue.They are designed with high freedoms to meet mission requirements, which increases manipulation complexity.By using a simulated China Space Station robotic arm platform, this study systematically compared four interaction methods (direct joint control, and icon-, joystick-, and voice-based end-effector control) in fine adjustmenttasks under different task complexity and time pressure conditions. The results show that interaction method and task complexity significantly affected both operational efficiency and accuracy. Iicon and joystick controls were more efficient in task completion, with lower subjective workload, higher EDA tonic data (SCL) and higher LF/HF ratio, while icon and voice controls resulted in fewer collisions. Increase of time pressure shortened completion time but caused more collisions. Task complexity caused an increase of task completion time. These findings could help selection of interaction methods and risk analysis of robotic arm operations.

Human performance in rendezvous and docking (RVD) tasks is critical for space missions, where human operators must maintain both efficiency and precision under constrained feedback conditions. Despite its operational importance, the effects of system parameters on RVD teleoperation remain insufficiently quantified. This study investigated how operation sensitivity, time delay and deviation tolerance affect performance and operators' physiological responses in RVD tasks. Thirty-six participants completed an RVD teleoperation task in a simulator, during which we recorded performance, behavioural and physiological measures. Results showed that reducing time delay improved docking speed, translational and angular efficiency, and reduced control inputs, error rate and visual attention. Tighter deviation tolerance reduced translational and angular deviation. Increasing operation sensitivity accelerated task execution but was accompanied by more control inputs, higher error rate and higher visual attention, elevated workload, and stress level. These findings provide empirical guidance for configuring teleoperation systems considering efficiency, controllability and operator workload.

Human reliability analysis (HRA) examines human errors or human failure events (HFEs) and their probabilities (Human Error Probabilities, HEPs) under specific task conditions using various models. Given the scarcity of empirical data, expert judgments remain vital in the HRA community. Despite prior efforts to model the expert elicitation process using Bayesian methods, there is still a lack of comprehensive understanding of the overall process and its key components in the Bayesian context, which undermines the effectiveness and applicability of these models. To address this gap, this study starts by investigating the sources of uncertainties in various HRA constructs and elements of the expert elicitation process, aiming to form rigorous definitions in probabilistic terms. Furthermore, practical elicitation approaches are modeled and common misunderstandings about Bayesian modeling in past HRA research are examined and addressed. Based on this foundation, a Bayesian model is developed to integrate the different elements with the HRA constructs, and the probabilistic relationships between them are analyzed and elucidated. Two direct applications of the model are also provided to demonstrate the model's practical applicability, highlighting its strengths and limitations. The proposed model not only offers a formal approach to rigorously incorporating expert judgments into HRA but also enhances researchers' and practitioners' understanding of the uncertainties related to the expert elicitation process, thereby providing a solid theoretical foundation for the development of future Bayesian models.

The industry is shifting from rigid systems towards dynamically adjustable architectures with flexible degrees of automation (DOA). A decision-role structure (DRS) defines how decision authority and responsibility for DOA adjustments are allocated between humans and machines. This study examined four DRSs, including human-only (HO), machine-only (MO), serial decision (SD) and parallel decision (PD) in simulated submarine track management tasks. The HO condition exhibited fewer DOA adjustments and prolonged high DOA, yielding better routine performance but poorer situation awareness (SA) and takeover performance. Participants tended to increase DOA under both SD and PD; downward requests were often rejected under SD and delegated to the machine under PD. The PD condition showed higher proportion of effective adjustments, higher SA and better takeover performance without compromising routine performance. The MO condition involved more manual operations and yielded suboptimal outcomes. Overall, the PD condition best balanced routine and takeover performance through proactive adjustments.

This study aims to analyze human-related incidents in nuclear power plant commissioning to inform human reliability analysis (HRA) and safety management following the integrated methodology reported in the preceding Part I article. This Part II article presents the key results, yielding several important insights. First, the study underscores the significance of knowledge-based and inter-team errors, both of which have been insufficiently explored in HRA research. In addition, the human error patterns observed during commissioning closely aligned with those in the test and maintenance tasks, indicating the potential for analyzing human errors in ex-control room activities under a unified framework. This study demonstrates how to clarify the associations between performance shaping factors (PSFs), including direct causality, mediating effects, confounding effects, and interaction-induced collider effects, by combining incident data with survey data, with the first three being more prevalent. These different associations have significant implications for HRA researchers and practitioners. Finally, the study showed that the associations between PSFs and human errors in incident data can be used to infer the relative impacts of individual PSFs across different cognitive function failures. A comparison of these inferences with existing HRA methods revealed considerable alignment, suggesting a promising avenue for validating PSF multipliers in current HRA frameworks. In summary, with its high ecological validity, incident data can offer valuable insights for improving HRA methodologies and safety management practices.

This study aims to take advantage of incident data to enhance human reliability analysis (HRA) and safety management, with the research presented in two parts. Part I of this article reports the methodology for incident coding and analysis. Generally, human-related incident analysis aims to answer WHAT can go wrong (the failed task), HOW it can go wrong (the human error mode), and WHY it went wrong (the contextual factors). To address these questions, an HRA framework focusing on generic task types (GTTs), cognitive failure modes (CFMs) and performance shaping factors (PSFs), was applied to code 133 incidents in nuclear power plant commissioning. A multidisciplinary expert team comprising HRA, domain, and integrative experts conducted the coding through a structured three-stage process, ensuring reliability and validity. Statistical association tests were conducted to explore the relationships between different PSFs and between PSFs and CFMs along with our interpretations. Regarding PSF inter-associations, a supplementary questionnaire survey was administered to capture the strengths of these associations in general tasks. The combined incident and survey data helped to clarify the origins of the association, including direct causality, mediating effects, confounding effects, and interactioninduced collider effects. Additionally, PSF-CFM associations were used to infer the relative impact of PSFs across different cognitive functions, offering a new approach for validating PSF multipliers in HRA.

Manipulator at a space station provides great help for extravehicular activities by moving astronauts or various loads outside the space station. Its teleoperation has high level of safety and efficiency requirements. This study extracts the process and characteristics of emergency rescue tasks and extravehicular activities of the space manipulator in China Space Station (CSS) and designs an emergency rescue task for an astronaut in the space station bulkhead to observe human behaviors and performance when manipulating the space manipulator under different conditions of task complexity and time pressure. The results demonstrated that task complexity significantly extended task completion time, with a notable interaction between time pressure and complexity at higher levels. Logistic regression indicates that higher complexity generally improved completion rates. Collision frequency and success rates were influenced by both factors, necessitating improved operational support for safety. Time pressure increased workload ratings, while task complexity's impact was minimal due to inherent challenges of teleoperation task. This study provided valuable insights into the interplay between task complexity and time pressure in space manipulator teleoperation.

With the rapid development of automation and intelligent systems, human-machine teaming (HMT) is becoming increasingly important in various fields. With a large body of research focusing on this topic for different application domains, there is a need to summarize general design principles for HMT. This review serves for this need. Through a systematic review, we identify the scope of the narrative review, and then through the narrative review methodology, we summarize various design guidelines from HCI, Human-Robot Interaction (HRI), and specific HMT scenarios. As a result, this paper presents general principles for HMT design, as well as exploring how to define the HMT process, how to design effective prompting and alerting mechanisms, and what collaborative approaches are available. These results provide a theoretical foundation and practical guidance for the development of future HMT systems.

This paper focused on flexible human-machine collaboration in complex systems, which aims to explore all feasible or desired function allocation and collaboration solutions in the design. A three-level framework, including labour division, mutual assistance, and joint performance, is proposed to help flexible human-machine collaboration analysis. Then this study illustrates the above concept and idea with a lunar surface sampling case study, including task decomposition, analysis of human and machine capabilities for each functional unit, and determining collaboration solutions and their applicable situations. These solutions enable dynamic function allocation, enhancing system adaptability and inspiring future human-machine system designs for lunar exploration.

The conventional approaches to assessing mental workload with operators are time-consuming, and even more challenging when experienced operators are tougher to find. Prior to an experiment involving operators, mental workload prediction methods may be useful for having a preliminary evaluation of a system or interface. This study represented mental workload using the ratio of GOMS-based predicted task completion time to available time. The low-version and high-version maritime operation interfaces were compared. In the GOMS analysis, this study disassembled task goals based on a hierarchical structure and matched each subtask goal with a method. Given the presence of considerable repetitive GOMS operators throughout the task execution, the idea of operator sequence block was introduced for task analysis and reader comprehension. By nesting these blocks, the task was decomposed into keystroke-level GOMS operators. By accumulating the standard times of the GOMS operators, the time prediction results for operator sequence blocks, methods, hierarchical task goals, and overall task can be obtained following the bottom-up approach.The results indicated that the number of GOMS operators and the task completion time required for the operators significantly decreased when using the high-version interface. Consequently, it was anticipated that the high-version interface could notably reduce the operators’ mental workload. The mental workload prediction method based on GOMS proposed in this study can be used to guide early-stage interface design to enhance operator performance.

By conducting a mixed-design experiment using simplified accident handling tasks performed by two-person teams, this study examined the effects of automation function and condition (before, during, and after malfunction) on human performance. Five different and non-overlapping functions related to human information processing model were considered and their malfunctions were set in a first-failure way. The results showed that while the automation malfunction impaired task performance, the performance degradation for information analysis was more severe than response planning. Contrary to other functions, the situation awareness for response planning and response implementation tended to increase during malfunctioning and decrease after. In addition, decreased task performance reduced trust in automation, and malfunctions in earlier stages of information processing resulted in lower trust. Suggestions provided for the design and training related to automation emphasise the importance of high-level cognitive support and the benefit of involving automation error handling in training.

Human-machine function allocation is the process of determining how a system functions or tasks are distributed between humans and machines. Reasonable human-machine function allocation is a key factor in ensuring system safety and performance. Considering the deficiency of existing methods of human-machine function allocation, this paper proposes a generalized framework for human-machine function allocation covering the static and dynamic function allocation phases. The functional units formed by task decomposition engage in the framework as input. The function allocation solution space is first established based on the consideration of strengths and weaknesses of humans and machines and the task requirements to their capabilities. Then feasible solution space is formed in response to situational factors to implement a flexible human-machine function allocation, so as to provide more possibilities for timely and effective response to various possible safety problems. Finally, optimal solution is determined by comprehensive evaluation with trade-off criteria and relative suitability rules of humans and machines to realize safer and more efficient human-machine collaboration. In addition, the limitation and preference rules in terms of human and machine capabilities, situational feasibility rules established with situational triggering indicators, a comprehensive evaluation with trade-off criteria and relative suitability rules of humans and machines are summarized to illustrate the application of the framework.

The recent Human Reliability Analysis (HRA) method developed by U.S. Nuclear Regulatory Commission, Integrated Human Event Analysis System (IDHEAS), provides a well-grounded model to analyze human errors as well as a promising interface for generalizing and integrating human error data from various sources. Nevertheless, the task context and human performance in different domains, even in different applications within one domain, manifest quite different features which make it elusive to draw an inter-sector or inter-application comparison. In light of this contradiction, one question arises: Can HRA in distinct applications be cross-referenced to one another? Motivated by this, an interview study was conducted as a preliminary attempt to figure out the similarities and differences between two typical activities in the nuclear domain, i.e., maintenance and commissioning, with respect to two vital HRA elements, i.e., error modes and performance shaping factors (PSFs). A total of 21 engineers in a nuclear power plant were recruited to participate in the interview about salient error modes and PSFs in maintenance activities as well as a comparison with commissioning activities. Results show that the two activities share analogous error modes and PSFs on the whole, but vary in the patterns and distributions of each individual PSFs. Hence, results of the present study indicate that human errors in these two comparable activities could be characterized by a unified taxonomy, affording positive evidence for the thrown question. However, the discrepancy in the PSF patterns should never be neglected, which could lead to the divergency of nominal PSF levels and nominal human error probabilities.

To ensure safe and efficient collaboration between humans and automated systems, it's essential to understand how to allocate functions dynamically. The first step involves identifying the factors that may trigger such dynamic function allocation. When factors influencing information processing emerge and reach a certain level of significance, individuals often experience changes in their states and performance. It is at this juncture that dynamic human-machine function allocation becomes necessary. Therefore, identifying these factors from the perspective of human information processing can help determine whether a task element will be affected by a specific factor and if dynamic function allocation should be initiated. By conducting an extensive literature review encompassing various related studies, this research identified factors that may impact different stages of human information processing. Subsequently, these factors were identified for dynamic function allocation in human-machine collaboration. This study is expected to provide a theoretical foundation for achieving dynamic function allocation in human-machine teaming.

In complex social-technical systems, human teams are required to handle automation errors to maintain system performance. This study developed two path models that connected the team process variables with the automation error management process, based on data collected from a previous experiment. The models show that trust has negative effects on most of the management activities. Team situation awareness could facilitate the whole process by supporting information selecting activity during the analysis and planning stage. Better teamwork could assist the stages of error detection and analysis and planning. Teams with more balanced decision making and workload would find it easier to understand and handle an automation error. The results also reveal that forming an interpretation of the error is helpful but not a necessity for error management. Path models developed in this study provide a technical basis for better team processes in the face of automation error.

为进一步厘清人的因素(HF)与系统工程(SE)未能很好的集成这一研究缺口,探索HF与SE的有效集成方案,基于制定的工业实践经验筛选方法,开展了HF与SE集成的技术路线总结.针对前述研究缺口,提出了相应的人与系统集成(HSI)解决方案,开展了 3 个方面的HF与SE集成的讨论,并进行了相应的研究展望.研究结果可用于加深学术/工业界对复杂工业系统实践中HF与SE集成的认识,并为解决前述研究缺口提供参考.

为更好地厘清人的因素(HF)与系统工程(SE)未能很好集成这一研究缺口,对典型安全关键领域相关组织/机构中涉及HF与SE集成的相关术语进行了定义总结和概念辨析.结果表明:不同领域、不同区域组织/机构的话语体系不一致,存在术语定义不统一、相似术语混用等问题;HF与SE集成的相关术语(人因工程(HFE)、人因/工效(HF/E)、人与系统集成/人因集成(HSI/HFI)、以人为中心的设计(HCD))之间存在一定的差异,简单总结为 HFE≤HF/E、HF/E与HSI/HFI相关联以及HCD≤HFE≤HSI/HFI,学术/工业界对这种差异的认识还略显不足.本研究可为加深研究人员对HF与SE集成知识的认识提供一定参考.