Teresa Gutiérrez

Evaluating virtual reality and augmented reality training for industrial maintenance and assembly tasks

The current study evaluated the use of virtual reality (VR) and augmented reality (AR) platforms, developed within the scope of the SKILLS Integrated Project, for industrial maintenance and assembly (IMA) tasks training. VR and AR systems are now widely regarded as promising training platforms for complex and highly demanding IMA tasks. However, there is a need to empirically evaluate their efficiency and effectiveness compared to traditional training methods. Forty expert technicians were randomly assigned to four training groups in an electronic actuator assembly task: VR (training with the VR platform twice), Control-VR (watching a filmed demonstration twice), AR (training with the AR platform once), and Control-AR (training with the real actuator and the aid of a filmed demonstration once). A post-training test evaluated performance in the real task. Results demonstrate that, in general, the VR and AR training groups required longer training time compared to the Control-VR and Control-AR groups, respectively. There were fewer unsolved errors in the AR group compared to the Control-AR group, and no significant differences in final performance between the VR and Control-VR groups, probably due to a ceiling effect created by the use of two training trials in the selected task for participants who were expert technicians. The results suggest that use of the AR platform for training IMA tasks should be encouraged and use of the VR platform for that purpose should be further evaluated.

Simultaneous remote haptic collaboration for assembling tasks

Stand-alone virtual environments (VEs) using haptic devices have proved useful for assembly/disassembly simulation of mechanical components. Nowadays, collaborative haptic virtual environments (CHVEs) are also emerging. A new peer-to-peer collaborative haptic assembly simulator (CHAS) has been developed whereby two users can simultaneously carry out assembly tasks using haptic devices. Two major challenges have been addressed: virtual scene synchronization (consistency) and the provision of a reliable and effective haptic feedback. A consistency-maintenance scheme has been designed to solve the challenge of achieving consistency. Results show that consistency is guaranteed. Furthermore, a force-smoothing algorithm has been developed which is shown to improve the quality of force feedback under adverse network conditions. A range of laboratory experiments and several real trials between Labein (Spain) and Queen’s University Belfast (Northern Ireland) have verified that CHAS can provide an adequate haptic interaction when both users perform remote assemblies (assembly of one user’s object with an object grasped by the other user). Moreover, when collisions between grasped objects occur (dependent collisions), the haptic feedback usually provides satisfactory haptic perception. Based on a qualitative study, it is shown that the haptic feedback obtained during remote assemblies with dependent collisions can continue to improve the sense of co-presence between users with regard to only visual feedback.

Training of Procedural Tasks Through the Use of Virtual Reality and Direct Aids

A high percentage of the human activities are based on procedural tasks, for example cooking a cake, driving a car, fixing a machine, etc. Reading an instruction book, watching a video or listening the explanation from an expert have been the traditional methods to learn procedural tasks. But, most researchers agree that procedural tasks are learnt gradually as a result of practice through repeating exposures to the task.

Training strategies for learning a 3D trajectory with accuracy

The goal of this study was to evaluate different learning conditions for motor skill transfer. The study was divided into two experiments with the same task: learning a 3D trajectory with accuracy. The first experiment was focused on evaluating the efficiency of three feedback schemes for the target trajectory: visual, haptic and visual-haptic feedback. The second experiment was focused on analyzing the influence of decreasing the feedback during the training process. The results suggest that the best learning condition for learning a 3D trajectory with accuracy is to provide visual-haptic feedback, which facilitates the understanding of the dimension and orientation of each trajectory segment and solves any visual discrepancies that may exist. Furthermore, although continuous feedback can create dependences in users and impede the transfer of motor skills, feedback based on user request can also be dangerous since users can create a wrong mental representation that keep them from replicating the trajectory accurately. Therefore, when the performance of a task depends on references created during the training process, it seems appropriate for the system to provide automatic feedback based on user performance.

Optimisation of Electromagnetic Design using HPCN

The development of HPCN and portable parallel implementations through BSP has encouraged developers and users of electromagnetic design software to address both the “grand challenge” problems and replace analysis of a design with synthesis.

An environment for the optimization of electromagnetic design

In this paper a short description of the EPOCH design optimization environment will be given and results on the parallelization of the electromagnetic analysis codes based on the Bulk Synchronous Parallel approach, as well as testing of the environment using industrially relevant 2d and 3d test cases, such as the design of a microwave oven, eddy current brake, advanced accelerator magnet and thin film recording head, will be presented.

Learning Force Patterns With a Multimodal System Using Contextual Cues

Abstract Previous studies on learning force patterns (fine motor skills) have focused on providing “punctual information”, which means users only receive information about their performance at the current time step. This work proposes a new approach based on “contextual information”, in which users receive information not only about the current time step, but also about the past (how the target force has changed over time) and the future (how the target force will change). A test was run to compare the performance of the contextual approach in relation to the punctual information, in which each participant had to memorize and then reproduce a pattern of force after training with a multimodal system. The findings suggest that the contextual approach is a useful strategy for force pattern learning. The advantage of the contextual information approach over the punctual information approach is that users receive information about the evolution of their performance (helping to correct the errors), and they also receive information about the next forces to be exerted (providing them with a better understanding of the target force profile). Finally, the contextual approach could be implemented in medical training platforms or surgical robots to extend the capabilities of these systems.