Scientific and Technical Aerospace Reports Volume 39 April 6, 2001

20010023141 Science Applications International Corp., Center for Intelligent Systems, Littleton, CO USA
Precision Terminal Guidance for a Mars Lander
Klarquist, William N., Science Applications International Corp., USA; Wahl, Beth E., Science Applications International Corp., USA;
Lowrie, James W., Science Applications International Corp., USA; Concepts and Approaches for Mars Exploration; July 2000, Part 1, pp.
178-179; In English; See also 20010023036; No Copyright; Avail: CASI; A01, Hardcopy; A03, Microfiche
To date Mars landers have relied solely on Earth-based navigation measurements to achieve a desired landing site. They’ve
had no active guidance and control system to monitor and control the entry and descent trajectory or guide the final landing. This
results in very large landing site uncertainties (>180 km x 20 km) and precludes targeting specific, small scale regions such as
canyons and flood channels. Moreover, localized hazards cannot be sensed or avoided, resulting in higher mission risk. SAIC’s
Center for Intelligent Systems, (SAIC-CIS) based on current and past research, believes that reliably accurate landings at pre-selected
sites are achievable and that the mission risk associated with local hazards can be greatly reduced. Our concept involves
applying an integrated system level solution that leverages the tremendous amount of information available on the Martian environment
and applies modern technologies in the areas of visual based navigation, maneuverable parachutes, and advanced sensors.
Derived from text
Active Control; Terminal Guidance; Spacecraft Guidance; Mars Landing
20010024032 Colorado State Univ., Dept. of Mechanical Engineering, Fort Collins, CO USA
Interactive Representation Based Minimalist Robot
Bearden, Keith L.; Nov. 16, 2000; 259p; In English
Report No.(s): AD-A384636; No Copyright; Avail: CASI; A12, Hardcopy; A03, Microfiche
Behavior-based robotics have made great advancements in the area of autonomous robotic control. The Subsumption Architecture
and its derivatives have proven to be a most effective architecture for autonomous navigation in complex, dynamic environments.
Subsumption, however, has no interconnection between the behaviors and no flexibility for the hard-wired behaviors,
which drastically limit the ability of subsumption to achieve high level goals. Coupled to this lack of interconnection is the inability
of subsumption, or any currently available architecture, to possess system detectable error. This research proposes a fundamentally
different type of representation called Interactive Representation. Systems that possess Interactive Representation do not explicitly
represent the external environment; these systems represent interactive possibilities with the environment. This development,
based only upon interactive possibilities, opens up the ability of a system to possess system detectable error. An architecture was
developed based on Interactive Representation, the Interactive Representation Architecture (IRA). Error is based upon a functional
definition of task achievement, not upon detection of a sensory fault. There is never any attempt to compare one sensor to
another sensor for validation. An error is then defined as anything that causes the circularity of the organization of a system to
become broken. This definition of error is implicit instead of explicit, which allows the IRA based systems to truly possess system
detectable error and to be able to recover from those errors without the use of an external observer, without resorting to plans, and
without increasing the complexity or reducing the speed. Both real and simulated systems utilizing this implicit definition of error
were proven to be superior to systems not possessing system detectable error even when there were no faults present.
DTIC
Robotics; Robots; Error Detection Codes; Computerized Simulation; Control Theory
20010024107 Chicago Univ., Chicago, IL USA
Investigation of Genetic Algorithms for Computer-Aided Diagnosis Annual Report, 1 Oct. 1997 - 30 Sep. 1998
Kupinski, Matthew A.; Oct. 1998; 27p; In English
Contract(s)/Grant(s): DAMD17-97-1-7202
Report No.(s): AD-A369253; No Copyright; Avail: CASI; A03, Hardcopy; A01, Microfiche
Computer-aided diagnosis has the potential of substantially increasing diagnostic accuracy in mammography. Using a computer
to double-check a radiologist’s findings is becoming more popular and more important as the public learns that the best
defense against breast cancer is early detection. The University of Chicago is currently developing computerized schemes to detect
cancers in digital mammograms. We use a pattern recognition system known as an artificial neural network (ANN) to classify
certain regions of the digital mammograms as cancerous or non-cancerous. ANNs are trained pattern recognition devices that take,
as inputs, features extracted from regions in the mammograms and output the classification. Currently, there are a total of 71 features
extracted from the various regions in each mammogram. A subset of those 71 features must be chosen as inputs for the ANN.
The goal of the proposed research is to apply a technique known as a genetic algorithm and other optimization techniques to find
the subset of features which would result in the best ANN performance. by improving the inputs to the ANN, the performance
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