Future space operations will increasingly demand cooperation between humans and autonomous space systems such as robots, observer satellites, and distributed components. Human team members use a combination of gestures, gaze, posture, deictic references and speech to communicate effectively. When a human team collaborates on a given task, they discuss the task, create a plan and then review this plan prior to execution to ensure success. This is exactly the process we envision for effective human-autonomous agent collaborative teamwork. Visual spatial information is a common reference for increased shared situation awareness between humans and autonomous systems. We use a spatial dialog approach to enable multiple humans to naturally and effectively communicate and work with multiple autonomous space systems. With this in mind, we have created a prototype spatial dialog system to support teamwork between humans and autonomous robotic agents. We combine augmented reality gesture interaction and visualization with speech to realize a spatial dialog capability. This paper describes our prototype, general approach, and related issues for team-oriented spatial dialog interaction with autonomous space systems.

We present visualization tools for analyzing molecular simulations of liquid crystal (LC) behavior. The simulation data consists of terabytes of data describing the position and orientation of every molecule in the simulated system over time. Condensed matter physicists study the evolution of topological defects in these data, and our visualization tools focus on that goal. We first convert the discrete simulation data to a sampled version of a continuous second-order tensor field and then use combinations of visualization methods to simultaneously display combinations of contractions of the tensor data, providing an interactive environment for exploring these complicated data. The system, built using AVS, employs colored cutting planes, colored isosurfaces, and colored integral curves to display fields of tensor contractions including Westin's scalar cl, cp, and cs metrics and the principal eigenvector. Our approach has been in active use in the physics lab for over a year. It correctly displays structures already known; it displays the data in a spatially and temporally smoother way than earlier approaches, avoiding confusing grid effects and facilitating the study of multiple time steps; it extends the use of tools developed for visualizing diffusion tensor data, re-interpreting them in the context of molecular simulations; and it has answered long-standing questions regarding the orientation of molecules around defects and the conformational changes of the defects.

Researchers in computational condensed matter physics deal with complex data sets consisting of time varying 3D tensor, vector, and scalar quantities. Particularly, in the research of topological defects in nematic liquid crystals (LC) displaying the results of the computer simulation of molecular dynamics presents a challenge. Combining existing immersive and interactive visualization methods we developed new methods that attempt to provide a clear, efficient, and intuitive way to visualize and explore LC data. In addition, the visualization of the data has presented us with a novel method of obtaining the locations of the topological defects present in a liquid crystal system.