Mixed Reality Experiments Research Articles

Lessons learnt running distributed and remote mixed reality experiments

One traditional model of research on mixed-reality systems, is the laboratory-based experiment where a number of small variants of a user experience are presented to participants under the guidance of an experimenter. This type of experiment can give reliable and generalisable results, but there are arguments for running experiments that are distributed and remote from the laboratory. These include, expanding the participant pool, reaching specific classes of user, access to a variety of equipment, and simply because laboratories might be inaccessible. However, running experiments out of the laboratory brings a different set of issues into consideration. Here, we present some lessons learnt in running eleven distributed and remote mixed-reality experiments. We describe opportunities and challenges of this type of experiment as well as some technical lessons learnt.

Ubiq-exp: A toolkit to build and run remote and distributed mixed reality experiments

Developing mixed-reality (MR) experiments is a challenge as there is a wide variety of functionality to support. This challenge is exacerbated if the MR experiment is multi-user or if the experiment needs to be run out of the lab. We present Ubiq-Exp - a set of tools that provide a variety of functionality to facilitate distributed and remote MR experiments. We motivate our design and tools from recent practice in the field and a desire to build experiments that are easier to reproduce. Key features are the ability to support supervised and unsupervised experiments, and a variety of tools for the experimenter to facilitate operation and documentation of the experimental sessions. We illustrate the potential of the tools through three small-scale pilot experiments. Our tools and pilot experiments are released under a permissive open-source license to enable developers to appropriate and develop them further for their own needs.

Delay induced swarm pattern bifurcations in mixed reality experiments.

Swarms of coupled mobile agents subject to inter-agent wireless communication delays are known to exhibit multiple dynamic patterns in space that depend on the strength of the interactions and the magnitude of the communication delays. We experimentally demonstrate communication delay-induced bifurcations in the spatiotemporal patterns of robot swarms using two distinct hardware platforms in a mixed reality framework. Additionally, we make steps toward experimentally validating theoretically predicted parameter regions where transitions between swarm patterns occur. We show that multiple rotation patterns persist even when collision avoidance strategies are incorporated, and we show the existence of multi-stable, co-existing rotational patterns not predicted by usual mean field dynamics. Our experiments are the first significant steps toward validating existing theory and the existence and robustness of the delay-induced patterns in real robotic swarms.

Collaborative Educational Environments Incorporating Mixed Reality Technologies: A Systematic Mapping Study

In this paper, we report findings from a systematic mapping study, conducted to review the existing literature on collaborative educational environments incorporating mixed reality technologies. There is increasing interest in mixed reality technologies in education, especially with the introduction of new over head mounted displays (OHMDs), such as HoloLens, Oculus Rift, and HTC Vive. with the consideration of areas, such as education, dynamic technology, and complex environments, a research area is identified. We carried out an extensive review of the literature from 2007 to 2017 and conducted an analysis of the works on mixed reality technologies and its subcategories applied to collaborative education environments. Results highlighted the lack of research across the mixed reality spectrum, especially in the augmented virtuality subcategory, as well as technical limitations such as response time in the development of mixed reality technologies for collaborative environments. Furthermore, the difficulty of teaching professionals to replicate mixed reality experiments in real environments, due to the technical skills required, was identified. The main contribution of this paper is the discussion of the current works with visualization of the present state of the area, which is aimed to encourage educators to develop mixed reality artefacts and conduct further research to support collaborative educational environments.

Persistent and Robust Execution of MAPF Schedules in Warehouses

Multi-agent path finding (MAPF) is a well-studied problem in artificial intelligence that can be solved quickly in practice when using simplified agent assumptions. However, real-world applications, such as warehouse automation, require physical robots to function over long time horizons without collisions. We present an execution framework that can use existing single-shot MAPF planners and ensures robust execution in the presence of unknown or time-varying higher-order dynamic limits, unforeseen robot slow-downs, and unpredictable obstacle appearances. Our framework also naturally enables the overlap of re-planning and execution for persistent operation and requires little communication between robots and the centralized planner. We demonstrate our approach in warehouse simulations and in a mixed reality experiment using differential drive robots. We believe that our solution closes the gap between recent research in the artificial intelligence community and real-world applications.

Path Planning for Multiple Agents under Uncertainty

Multi-agent systems in cluttered environments require path planning that not only prevents collisions with static obstacles, but also safely coordinates the motion of many agents. The challenge of multi-agent path finding becomes even more difficult when the agents experience uncertainty in their pose. In this work, we develop a multi-agent path planner that considers uncertainty, called uncertainty M* (UM*), which is based on a prior multi-agent path approach called M*. UM* plans a path through the belief space for each individual agent and then uses a strategy similar to M* that coordinates only agents that are “likely” to collide. This approach has the same scalability advantages as M*. We then introduce an extension called Permuted UM* (PUM*) that uses randomized restarts to enhance performance. We finish by presenting a belief space representation appropriate for multi-agent path planning with uncertainty and validate the performance of UM* and PUM* in simulation and mixed-reality experiments.

トラフィックシミュレータとドライビングシミュレータを連携した「複合現実感交通実験スペース」の構築

トラフィックシミュレータとドライビングシミュレータを連携した「複合現実感交通実験スペース」の構築