Publications

We summarize the results of a project to develop evolutionary computing methods for the design of behaviors of embodied agents in the form of autonomous vehicles. We conceived and implemented a strategy called graduated embodiment. This method allows high-level behavior algorithms to be developed using genetic programming methods in a low-fidelity, disembodied modeling environment for migration to high-fidelity, complex embodied applications. This project applies our methods to the problem domain of robot navigation using adaptive waypoints, which allow navigation behaviors to be ported among autonomous mobile robots with different degrees of embodiment, using incremental adaptation and staged optimization. Our approach to biomimetic behavior engineering is a hybrid of human design and artificial evolution, with the application of evolutionary computing in stages to preserve building blocks and limit search space. The methods and tools developed for this project are directly applicable to other agent-based modeling needs, including climate-related conflict analysis, multiplayer training methods, and market-based hypothesis evaluation.

The emerging field of haptics represents a fundamental change in human-computer interaction (HCI), and presents solutions to problems that are difficult or impossible to solve with a two-dimensional, mouse-based interface. To take advantage of the potential of haptics, however, innovative interaction techniques and programming environments are needed. This paper describes FGB (FLIGHT GHUI Builder), a programming tool that can be used to create an application specific graphical and haptic user interface (GHUI). FGB is itself a graphical and haptic user interface with which a programmer can intuitively create and manipulate components of a GHUI in real time in a graphical environment through the use of a haptic device. The programmer can create a GHUI without writing any programming code. After a user interface is created, FGB writes the appropriate programming code to a file, using the FLIGHT API, to recreate what the programmer created in the FGB interface. FGB saves programming time and increases productivity, because a programmer can see the end result as it is created, and FGB does much of the programming itself. Interestingly, as FGB was created, it was used to help build itself. The further FGB was in its development, the more easily and quickly it could be used to create additional functionality and improve its own design. As a finished product, FGB can be used to recreate itself in much less time than it originally required, and with much less programming. This paper describes FGB's GHUI components, the techniques used in the interface, how the output code is created, where programming additions and modifications should be placed, and how it can be compared to and integrated with existing API's such as MFC and Visual C++, OpenGL, and GHOST.

Sandia Laboratories' computational scientists are addressing a very important question: How do we get insight from the human combined with the computer-generated information? The answer inevitably leads to using scientific visualization. Going one technology leap further is teraflop visualization, where the computing model and interactive graphics are an integral whole to provide computing for insight. In order to implement our teraflop visualization architecture, all hardware installed or software coded will be based on open modules and dynamic extensibility principles. We will illustrate these concepts with examples in our three main research areas: (1) authoring content (the computer), (2) enhancing precision and resolution (the human), and (3) adding behaviors (the physics).

The key to insight is coupling the power of the computer with unique skills of the human. At Sandia National Laboratories' Interaction Laboratory, we call this teraflop visualization. We are concentrating research in three main area: 1) using the computer as a facility for authoring content, 2) adding the physics to model real behaviors, and 3) allowing the human to utilize the improved precision and resolution provided by this new class of compute power.