ALBERT

Description: In the Fall of 1993, NASA Ames deployed a modified Phantom S2 Remotely-Operated underwater Vehicle (ROV) into an ice-covered sea environment near McMurdo Science Station, Antarctica. This deployment was part of the antarctic Space Analog Program, a joint program between NASA and the National Science Foundation to demonstrate technologies relevant for space exploration in realistic field setting in the Antarctic. The goal of the mission was to operationally test the use of telepresence and virtual reality technology in the operator interface to a remote vehicle, while performing a benthic ecology study. The vehicle was operated both locally, from above a dive hole in the ice through which it was launched, and remotely over a satellite communications link from a control room at NASA's Ames Research Center. Local control of the vehicle was accomplished using the standard Phantom control box containing joysticks and switches, with the operator viewing stereo video camera images on a stereo display monitor. Remote control of the vehicle over the satellite link was accomplished using the Virtual Environment Vehicle Interface (VEVI) control software developed at NASA Ames. The remote operator interface included either a stereo display monitor similar to that used locally or a stereo head-mounted head-tracked display. The compressed video signal from the vehicle was transmitted to NASA Ames over a 768 Kbps satellite channel. Another channel was used to provide a bi-directional Internet link to the vehicle control computer through which the command and telemetry signals traveled, along with a bi-directional telephone service. In addition to the live stereo video from the satellite link, the operator could view a computer-generated graphic representation of the underwater terrain, modeled from the vehicle's sensors. The virtual environment contained an animate graphic model of the vehicle which reflected the state of the actual vehicle, along with ancillary information such as the vehicle track, science markers, and locations of video snapshots. The actual vehicle was driven either from within the virtual environment or through a telepresence interface. All vehicle functions could be controlled remotely over the satellite link.

Description: Vision processing is one of the most computationally intensive tasks required of an autonomous robot. The data flow from a single typical imaging sensor is roughly 60 Mbits/sec, which can easily overload current on-board processors. Optical correlator-based processing can be used to perform many of the functions required of a general robotic vision system, such as object recognition, tracking, and orientation determination, and can perform these functions fast enough to keep pace with the incoming sensor data. We describe a hybrid digital electronic/analog optical robotic vision processing system developed at Ames Research Center to test concepts and algorithms for autonomous construction, inspection, and maintenance of space-based habitats. We discuss the system architecture design and implementation, its performance characteristics, and our future plans. In particular, we compare the performance of the system to a more conventional all digital electronic system developed concurrently. The hybrid system consistently outperforms the digital electronic one in both speed and robustness.

Description: An architecture for general-purpose optical neural network processor is presented in which the interconnections and weights are formed by directing coherent beams holographically, thereby making use of the space-bandwidth products of the recording medium for sparsely interconnected networks more efficiently that the commonly used vector-matrix multiplier, since all of the hologram area is in use. An investigation is made of the use of computer-generated holograms recorded on such updatable media as thermoplastic materials, in order to define the interconnections and weights of a neural network processor; attention is given to limits on interconnection densities, diffraction efficiencies, and weighing accuracies possible with such an updatable thin film holographic device.

Description: The potential of a DARPA-sponsored advanced processor, the Intel iWarp, for use in future SSF Data Management Systems (DMS) upgrades is evaluated through integration into the Ames DMS testbed and applications testing. The iWarp is a distributed, parallel computing system well suited for high performance computing applications such as matrix operations and image processing. The system architecture is modular, supports systolic and message-based computation, and is capable of providing massive computational power in a low-cost, low-power package. As a consequence, the iWarp offers significant potential for advanced space-based computing. This research seeks to determine the iWarp's suitability as a processing device for space missions. In particular, the project focuses on evaluating the ease of integrating the iWarp into the SSF DMS baseline architecture and the iWarp's ability to support computationally stressing applications representative of SSF tasks.

Description: A holographic implementation for neural networks is proposed and demonstrated as an alternative to the optical matrix-vector multiplier architecture. In comparison, the holographic architecture makes more efficient use of the system space-bandwidth product for certain types of neural networks. The principal network component is a thermoplastic hologram, used to provide both interconnection weights and beam direction. Given the updatable nature of this type of hologram, adaptivity or network learning is possible in the optical system. Two networks with fixed weights are experimentally implemented and verified, and for one of these examples the advantage of the holographic implementation with respect to the matrix-vector processor is demonstrated.