Object Manipulation in Virtual Reality Under Increasing Levels of Translational Gain

Room-scale Virtual Reality (VR) has become an affordable consumer reality, with applications ranging from entertainment to productivity. However, the limited physical space available for room-scale VR in the typical home or office environment poses a significant problem. To solve this, physical spaces can be extended by amplifying the mapping of physical to virtual movement (translational gain). Although amplified movement has been used since the earliest days of VR, little is known about how it influences reach-based interactions with virtual objects, now a standard feature of consumer VR. Consequently, this paper explores the picking and placing of virtual objects in VR for the first time, with translational gains of between 1x (a one-to-one mapping of a 3.5m*3.5m virtual space to the same sized physical space) and 3x (10.5m*10.5m virtual mapped to 3.5m*3.5m physical). Results show that reaching accuracy is maintained for up to 2x gain, however going beyond this diminishes accuracy and increases simulator sickness and perceived workload. We suggest gain levels of 1.5x to 1.75x can be utilized without compromising the usability of a VR task, significantly expanding the bounds of interactive room-scale VR

Navigation in Human Flows : Planning with Adaptive Motion Grid

International audienceAn important challenge for mobile robots is to navigate efficiently in human populated environments. In this context, we examine how human presence grids can be extended to model human motions, considering only embedded sensors. The proposed flow grid computes in each cell a discrete distribution of the human motion. The model is defined to take into account the most recent observations, so as to adapt to changes. More, it is expanded with a predictive motion pattern. Then we revisit the cost function of the A* pathplanning algorithm to take into account the risk of encountering humans. We compare the standard A* with variants exploiting the human presence likelihood [1] and the proposed flow grid. Experiments in simulation show that the Flow grid A* is able to compute paths minimizing the risk of navigating against human flows, and to adapt to their variations. Experiments with a mobile robot confirms the ability of the model to map human flows and to optimize paths

Using Head Tracking Data for Robust Short Term Path Prediction of Human Locomotion

ISSN:0302-9743ISSN:1611-334

Development of a virtual pet game using oculus rift and leap motion technologies.

Recent emerging technology with a Virtual Reality (VR) aspect is very research-driven as well as successful in commercial devices. Within it, the most advantageous technology is the Oculus Rift headset because of its light-weight, low-cost and high quality, which has potential to bring novel VR theory into practice. Furthermore, Leap Motion has emerged as a high precision bared hand tracker which supports VR integration. Thus, the combination of these technologies is promising in many application areas. Gaming is one of these, particularly the life simulation game genre because gaming not only bridges users to familiar technology but also gives them full immersion into the synthetic world. Among the many successful simulation games, the digital pet raising game genre has proven itself in the gamers’ community as well as in relation to advances in motion controller games. This has motivated the development of a virtual reality pet game. So, this research envisages to develop a prototype of a pet-raising game using the Unity game engine based on Leap Motion and Oculus Rift technologies. The prototype contains a variety of pet interactions including feeding, cleaning, throw-catch, tricks training, etc. to enhance the hand motion controlling of Leap Motion as well as playing with first person perspective to give full immersion in terms of VR. After that, the importance of game evaluation is justified via quantitative research approaches, aming to investigate into the interactive technologies like Leap Motion. Kinectimals game based on Xbox Kinect technology, was selected to compare two games in terms of motion controlling similarity. Two experiments which are similar procedure on the developed game and Kinectimals, are conducted in order to collect objective measures such as duration, task and failure rate; plus participant’s subjective reporting following three questionnaires, the After-Scenario Questionnaire (ASQ), IBM computer usability satisfaction questionnaire (CSUQ) and NASA-Task Load Index (NASA-TLX). Those questionnaires included standard questions and additional questions which are specific design for the prototype. Comparing to Kinectimals, the game achieved a high acceptable score in terms of workload, information and interface quality satisfaction. The final prototype received much positive feedback without simulator/motion sickness during long term playing and interface design. Moreover, beside the rich content game playing, some hand gestures including fist, face-up hand, throw-grab activities were the most reliable using Leap Motion. However, hand tracking issues were identified due to the lack of robustness, particular in dynamic gestures. As a result, main contribution is to make VR more accessible to ordinary people via gaming as well as how to apply the immersion into a specific game genre. In spite of some games/applications based on these technologies combinations, the serious experiments verifying their feasibility are limited, which makes this research worth to carry on. The experiment’s findings it is hoped contribute to promoting the pet game genre within a VR setting, in particular immersion role and motion controlling