Architecture of Human-controlled Arm Manipulator Operator Training System

Nowadays Industrial robots of different kinematic structures and functionality are widely used nowadays in different areas of industry and most of them function autonomously with preprogrammed set of actions or typical trajectories. Some special purposes mobile and stationary robots are human-controlled and in most cases require qualified operator skills. For example, in nuclear industry the mechanical and electro-mechanical “master arm – slave arm” manipulators are widely used. Modern approaches of control systems and human machine interfaces designs allow to eliminate direct copy of kinematic configuration of manipulator arms, add semi-automatic and automated control modes. Key feature of such manipulator systems is a presence of human in control loop, by reason of variety and complexity of manipulations and, in some cases, non-deterministic environment. Due to these factors, it is very desirable to form operator sustainable skills to control such manipulators, including learning process using training systems. This article describes general approaches and practical aspects of the operator training and qualification system for arm-based special purpose manipulators. The set of training system functions include: general manipulator control (basic motion) and grasping actions, manipulator operations in restricted working areas, operator actions using virtual reality helms and multi-monitor visualization, etc. The hardware and software architecture of training system should allow effective training process and teamwork of trainee and instructor to reach good results. The paper proposes ROS-based software part of training system with description of inter-process communication messages, data formats, integration with database and following analysis of trainee action.

Bridging the Experience Gap - How do we migrate Skills and Knowledge between the Generations?

This research is based on a systematic review of machine learning (ML) approaches for the cognitive load (CL) assessment of applications for unmanned aerial system (UAS) operator training. The review synthesises evidence on how ML techniques have been applied to assess CL using diverse data sources, including physiological signals (e.g., EEG, HRV), behavioural measures (e.g., eye-tracking), and performance indicators. It highlights the effectiveness of models such as Support Vector Machines (SVMs), Random Forests (RFs), and advanced deep learning (DL) architectures such as Long Short-Term Memory (LSTM), as well as how the use of different methods affects the performance of ML models, with studies reporting accuracies of up to 98%. The findings also indicate that, compared with traditional UAS training approaches, ML approaches can enhance training by providing adaptive assessment, with methodological factors such as model selection, data preprocessing, and validation being central to ML assessment performance. These findings highlight the value of accurate CL assessment as a foundation for adaptive training systems, supporting enhanced UAS operator performance and operational safety. By consolidating the methodological insights and identifying research gaps, this review provides valuable background information for advancing ML-based CL assessment and its integration into adaptive UAS operator training systems to enhance UAS operator training.

Design and implementation of a real-time training environment for protective relay

17 - Dynamic IGCC system simulator

Virtual environments (VEs) represent a computer-generated three-dimensional (3D) environment and are perceived to facilitate human perception and the comprehension of complex concepts and/or enhance the human ability to perform complex psychomotor tasks. A generic immersive VE possesses the features of 3D immersion, multisensory cues, and frame of reference (FOR). The overall virtual training (VT) system architecture uses a modular approach where different software modules process information independently. A multidimensional user-centered systematic training evaluation (MUSTe) method is adopted to assess cognitive, affective, and skill-based learning in the VT system. This chapter demonstrates that individual's characteristics and VT design features that can influence user task performance and cognitive learning outcomes, regardless of levels of expertise. The experiment suggested some of the complexities and problems encountered in applying individual difference research to the user-centered design of haptic-audio-visual training systems.

ePHASORsim tool offers real-time dynamic simulations for transmission and distribution power systems. Applications such as contingency studies, testing control devices, operator training, and SCADA system tests are examples for employing this tool. This paper describes the hardware and software architecture of the ePHASORsim and its development. The accuracy of the tool has been evaluated in comparison to other commercial, but non-real-time, simulation packages for both transmission and distribution systems. Its real-time performance has been tested with a time-step of 10ms on a real-time simulator for large-scale power systems in the order of 20000 buses, 5000 generators, and over 9000 control devices.

A plant operator performance evaluation system to analyze plant operation records during accident training and to identify and classify operator errors has been developed for the purpose of supporting realization of a training and education system for plant operators. A knowledge engineering technique was applied to evaluation of operator behavior by both event-based and symptom-based procedures, in various situations including event transition due to multiple failures or operational errors The system classifies the identified errors as to their single and double types based on Swain's error classification and the error levels reflecting Rasmussen's cognitive level, and it also evaluates the effect of errors on plant state and then classifies error influence, using “knowledge for phenomena and operations”, as represented by frames. It has additional functions for analysis of error statistics and knowledge acquisition support of “knowledge for operations”. The system was applied to a training analysis for a scram event in a BWR plant, and its error analysis function was confirmed to be effective by operational experts.

Dynamic load change operation education in air separation processes using a multivariable and nonlinear model

An advanced aircrew training system and electronic warfare officer training are discussed. Training system functions include training system management, administration, support, delivery, evaluation, quality assurance, analysis, design, and development. Designing the overall training system architecture to these basic functions and then allocating them to respective training system components ensures that these functions are operational when the total training system is fielded. The use of functional analysis, storyboarding, and process analysis aids in defining the functions and laying out the top level architecture for a given training system. This activity is carried out in the analysis and design phases of the instructional systems development process interfaced with the systems engineering process. >

Drop-out fuse is the most commonly used type of protection switch for 10kV distribution lines and distribution transformers. The operations of opening and closing drop-out fuses are high-risk processes, and such live maintenance accidents can be fatal. This paper presents a framework of a training system for drop-out fuse operation. The developed system is based on HoloLens mixed reality and speech recognition technology to train students for drop-out fuse operation. The system has two modules: drop-out fuse structure and drop-out fuse operation. Three modes of operation are included in each module: learning, practice, and evaluation, which can be visited according to the students’ level of skill. The proposed training system has low cost, occupies small space, can be used at any time, and guarantees the personal safety of students during the training operation.

This paper will introduce the development case study about virtual-reality-based operation training system for steel making process. Steel making process consist iron making process to create liquid steel, pig iron, by reduction process, steel making process to make molten steel by refining, continuous casting process to make slab, and rolling process to make final product like coil, plate. This steel making process deals with liquid and solid products, so facilities of steel making process are very various and complicated. In addition, according to various customer requirements, the recycle of facilities and recipes changing have been fast. So the training for skilled operators is very important point. In this paper, we develop steel making training system based virtual reality for training skilled operator. This system consists of virtual machine, virtual HMI, and virtual control panel. And for fitting the characteristics of each process and increasing the education effectiveness, we develop dynamic methods like the method of dynamic education system configuration, initial facilities setup operation education system, and etc.

Two factors that are critical aspects of complex system performance are system design and operator training. The contribution of each of these factors becomes paramount as increases in system complexity demand more sophisticated operator timesharing skills to monitor and control system operations safely and efficiently. In order to increase human operator skill levels, improvements in system design must be achieved to make aspects of monitoring and controlling tasks more commensurate with human abilities. Secondly, more effective and efficient training programs must be developed to allow human operators to acquire and maintain appropriate skill levels. Before these improvements can be accomplished, however, a greater understanding of the capabilities and limitations of the human information processing system must be obtained. In a typical modern system (e.g., aircraft cockpit, centralized control room of a power plant or battle management system) the human may perform many tasks concurrently, including visual search, memory search, retention and recall, comprehension, reasoning, judgement and decision making, and executing fine motor movements. Although some combinations of these kinds of tasks may be compatible and non-interfering, other task combinations may interfere and compete for the human's limited information processing capacity, creating overload conditions. The consequence of the human operator's low threshold for overload leads to increased probability of error, with subsequent deteriorations in system performance. There are two theoretical approaches to human attention and information processing that have implications for system design and operator training issues in the context of timesharing and high workload situations. Multiple resource theory and automatic/controlled processing theory have each stimulated much empirical work that provided useful data on human timesharing abilities. Such data have provided the basis for predicting task combinations and training requirements that allow high levels of performance under timesharing and high workload situations. The purpose of this symposium is to describe recent empirical developments that may further our understanding of human information processing capabilities and limitations, particularly under timesharing and high workload conditions. The set of papers includes theoretical and applied treatments of multiple resource and automaticity theories. The issues discussed include the advantages and disadvantages of spreading information input over different sensory modalities (e.g., visual and auditory), development of training methods to acquire sophisticated high level cognitive skills quickly and efficiently, measurement of information processing capacity required by specific task combinations, and a delineation of the characteristics and limitations of working memory.

The situated approach to situation awareness (SA) claims that system operators often offload information instead of constructing detailed, stable and internal representations of dynamic situations. Offloading, which can be more or less deliberate, is the attenuation of internal processes in favor of augmenting external ones. Operators do this to minimise costly internal processing and storage. This paper uses empirical evidence and phenomenological analysis to argue that situated SA presupposes a motor intentional core. This core refers to embodied skills whose function is to present states of the system as being a certain way. They include, for example, the skillful eye scan patterns of experts and various epistemic actions that involve manipulating objects in the physical world to make information available to other cognitive processes. Through these motor intentional skills, offloaded information can be accessed on an as-needed basis. We discuss metrics for assessing the motor intentional core, as well as implications for interface design and the training of system operators, arguing that many motor intentional skills can be taught directly.

This paper presents a prototype training simulator design developed in response to the expanding student population for unmanned system operators. The projected operators for unmanned systems are now being expanded beyond the traditional focus-group of pilot and pilot trainees. The broadening of the field from which unmanned system operators previously had been selected, and the increased mission support role of operators, also broadens the ground training requirements in order to achieve certification. The backbone of the simulator for this training system is a scalable architecture concept that is softwareintensive with loosely coupled training system elements. This common backbone for scalable application also results in common logistic support, meaning lower life cycle cost. The prototype training device design takes advantage of commercially off-the-shelf (COTS) hardware and software products already proven in fielded platforms. The training system design that responds to these requirements incorporates principles from device-based aircrew training as well as high engagement strategies from simulation and gaming. This design not only enhances unmanned system operator training, but also makes significant advancement of role player in-the-loop mission training.