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Remotely operating complex robotic mechanisms in unstructured natural environments is difficult at best. When the communications time delay is large, as for a Mars exploration rover operated from Earth, the difficulties become enormous. Conventional approaches, such as rate control of the rover actuators, are too inefficient and risky. The Intelligent Mechanisms Laboratory at the NASA Ames Research Center has developed over the past four years an architecture for operating science exploration robots in the presence of large communications time delays. The operator interface of this system is called the Virtual Environment Vehicle Interface (VEVI), and draws heavily on Virtual Environment (or Virtual Reality) technology. This paper describes the current operational version of VEVI, which we refer to as version 2.0. In this paper we will describe the VEVI design philosophy and implementation, and will describe some past examples of its use in field science exploration missions.

Biomolecules exhibit specific binding and high affinity for their ligands. These properties can be exploited to produce sensitive, specific, real-time sensors for analytes that cannot be readily monitored by other methods. Several technologies for environmental monitoring using proteins are currently being developed. We discuss specific challenges to practical application of a family of protein-based sensors derived from bacterial periplasmic binding proteins. We also present recent work to address these challenges.

This paper discussed how data mining techniques could give advantage to the robot in navigation, in terms of speed. The input of our navigation system is the sensory information collected by the robot's equipped landmark sensor and infra-red sensor, the process of the system is the proposed data mining technique, and the output of the system is the execution of the moving direction in a 2D Martian environment. The results demonstrate efficient goal-oriented navigation using data mining techniques.

Accurate root zone moisture control in microgravity plant growth systems is problematic. With gravity, excess water drains along a vertical gradient, and water recovery is easily accomplished. In microgravity, the distribution of water is less predictable and can easily lead to flooding, as well as anoxia. Microgravity water delivery systems range from solidified agar, water-saturated foams, soils and hydroponics soil surrogates including matrix-free porous tube delivery systems. Surface tension and wetting along the root substrate provides the means for adequate and uniform water distribution. Reliable active soil moisture sensors for an automated microgravity water delivery system currently do not exist. Surrogate parameters such as water delivery pressure have been less successful.

This paper describes a multi-disciplinary damage detection methodology that can aid in detecting and diagnosing a damage in a given structural system, not limited to the example of a rotating gear presented here. Damage detection is performed on the gear stress data corresponding to the steady state conditions. The normal and damage data are generated by a finite-difference solution of elastodynamic equations of velocity and stress in generalized coordinates1. The elastodynamic solution provides a knowledge of the stress distribution over the gear such as locations of stress extrema, which in turn can lead to an optimal placement of appropriate sensors over the gear to detect a potential damage. The damage detection is performed by a multi-function optimization that incorporates Tikhonov kernel regularization reinforced by an added Laplacian regularization term as used in semi-supervised machine learning. Damage is mimicked by reducing the rigidity of one of the gear teeth.

During 1995, we tested instruments and attempted a seed-to-seed experiment with Super-Dwarf wheat in the Russian Space Station Mir. Utah instrumentation included four IR gas analyzers (CO2 and H2O vapor, calculate photosynthesis, respiration, and transpiration) and sensors for air and leaf (IR) temperatures, O2, pressure, and substrate moisture (16 probes). Shortly after planting on August 14, three of six fluorescent lamp sets failed; another failed later. Plastic bags, necessary to measure gas exchange, were removed. Hence, gases were measured only in the cabin atmosphere. Other failures led to manual watering, control of lights, and data transmission. The 57 plants were sampled five times plus final harvest at 90 d. Samples and some equipment (including hard drives) were returned to earth on STS-74 (Nov. 20). Plants were disoriented and completely vegetative. Maintaining substrate moisture was challenging, but the moisture probes functioned well.

We examine the requirements for on-board aircraft sensor systems that would allow pilots to “see through” poor weather, especially fog, and land and rollout aircraft under conditions that currently cause flight cancellations and airport closures. Three visual aspects of landing and rollout are distinguished: guidance, hazard detection and hazard recognition. The visual features which support the tasks are discussed. Three broad categories of sensor technology are examined: passive millimeter wave (PMMW), imaging radar, and passive infrared (IR). PMMW and imaging radar exhibit good weather penetration, but poor spatial and temporal resolution. Imaging radar exhibits good weather penetration, but typically relies on a flat-earth assumption which can lead to interpretive errors. PMMW systems have a narrow field of view. IR has poorer weather penetration but good spatial resolution.

Development of new air revitalization system (ARS) technology can initially be performed in a subscale laboratory environment, but in order to advance the maturity level, the technology must be tested in an end-to-end integrated environment. The Air Revitalization Technology Evaluation Facility (ARTEF) at the NASA Johnson Space Center (JSC) serves as a ground test bed for evaluating emerging ARS technologies in an environment representative of spacecraft atmospheres. At the center of the ARTEF is a hypobaric chamber which serves as a sealed atmospheric chamber for closed loop testing. A Human Metabolic Simulator (HMS) was custom-built to simulate the consumption of oxygen, and production of carbon dioxide, moisture and heat by up to eight persons. A variety of gas analyzers and dew point sensors are used to monitor the chamber atmosphere and the process flow upstream and downstream of a test article. A robust vacuum system is needed to simulate the vacuum of space.

This paper features a set of advanced technologies for autonomy and intelligence in advanced inspection systems of facility operations. These technologies offer a significant contribution to set a path to establish a system and an operating environment with autonomy and intelligence for inspection, monitoring and safety via gas and ambient sensors, video mining and speech recognition commands on unmanned ground vehicles and other platforms to support operational activities in the Cryogenics Test bed and other facilities and vehicles. These advanced technologies are in current development and progress and their functions and operations require guidance and formulation in conjunction with the development team(s) toward the system architecture.

In planning for Exploration missions and developing the required suite of environmental monitors, the difficulty lies in down-selecting a multitude of technology options to a few candidates with exceptional potential. Technology selection criteria include conventional analytical parameters (e.g., range, sensitivity, selectivity), operational factors (degree of automation, portability, required level of crew training, maintenance), logistical factors (size, mass, power, consumables, waste generation) and engineering factors such as complexity and reliability. Other more subtle considerations include crew interfaces, data readout and degree of autonomy from the ground control center. We anticipate that technology demonstrations designed toward these goals will be carried out on the International Space Station, the end result of which is a suite of techniques well positioned for deployment during Exploration missions.

Remote Tower Sensor Systems are proof-of-concept prototypes being developed by NASA/Ames Research Center (NASA/ARC) with collaboration with the FAA and NOAA. RTSS began with the deployment of an Airport Approach Zone Camera System that includes real-time weather observations at San Francisco International Airport. The goal of this research is to develop, deploy, and demonstrate remotely operated cameras and sensors at several major airport hubs and un-towered airports. RTSS can provide real-time weather observations of airport approach zone. RTSS will integrate and test airport sensor packages that will allow remote access to real-time airport conditions and aircraft status.