No abstract

Virtual reality (VR) has been proved as a promising tool for industrial design, but the traditional VR interface of first-person perspective (1PP) is not efficient to support assemblability assessment in narrow assembly spaces. In this paper, we proposed the multi-perspectives interface (MPI) which integrates the 1PP and the third-person perspective (3PP) using the handheld world-in-miniature (WIM). The MPI allows users to simulate the assembly operations in a natural manner similar to 1PP, while providing users with an overview of the assembly status through the WIM to assess the assemblability with superior spatial awareness. Two studies were conducted to test the performance of the proposed MPI. The first study tested user’s interaction performance in MPI using a common interaction task, which reveals stronger spatial awareness in MPI than in 1PP without the cost of losing natural interaction. Based on the results of the first study, the second study tested the performance, usability, and workload of MPI in an assemblability assessment task. The results show the advantages of MPI in the reachability evaluation in the narrow spaces. The main contribution of this paper is improving the interface and user-interface interaction in VR-aided assembly assessment system to improve user’s interaction performance and assessment ability in narrow assembly spaces.

Accurate and informative hand-object collision feedback is of vital importance for hand manipulation in virtual reality (VR). However, to our best knowledge, the hand movement performance in fully-occluded and confined VR spaces under visual collision feedback is still under investigation. In this paper, we firstly studied the effects of several popular visual feedback of hand-object collision on hand movement performance. To test the effects, we conducted a within-subject user study (n=18) using a target-reaching task in a confined box. Results indicated that users had the best task performance with see-through visualization, and the most accurate movement with the hybrid of proximity-based gradation and deformation. By further analysis, we concluded that the integration of see-through visualization and proximity-based visual cue could be the best compromise between the speed and accuracy for hand movement in the enclosed VR space. On the basis, we designed a visual collision feedback based on projector decal,which incorporates the advantages of see-through and color gradation. In the end, we present demos of potential usage of the proposed visual cue.

Skeleton tracking based on multiple Kinects data fusion has been proved to have better accuracy and robustness than single Kinect. However, previous works did not consider the inconsistency of tracking accuracy in the tracking field of Kinect and the self-occlusion of human body in assembly operation, which are of vital importance to the fusion performance of the multiple Kinects data in assembly task simulation. In this work, we developed a multi-Kinect fusion algorithm to achieve robust full-body tracking in virtual reality (VR)-aided assembly simulation. Two reliability functions are first applied to evaluate the tracking confidences reflecting the impacts of the position-related error and the self-occlusion error on the tracked skeletons. Then, the tracking skeletons from multiple Kinects are fused based on weighted arithmetic average and generalized covariance intersection. To evaluate the tracking confidence, the ellipsoidal surface fitting was used to model the tracking accuracy distribution of Kinect, and the relations between the user-Kinect crossing angles and the influences of the self-occlusion on the tracking of different parts of body were studied. On the basis, the two reliability functions were developed. We implemented a prototype system leveraging six Kinects and applied the distributed computing in the system to improve the computing efficiency. Experiment results showed that the proposed algorithm has superior fusion performance compared to the peer works.