Autonomous Exploration Inc. Awarded a 2009 NASA SBIR

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Lunar Electric Rover


Autonomous Exploration Inc. has been awarded a 2009 NASA Phase I SBIR grant
for the proposal titled Advanced Bayesian Methods for Lunar Surface Navigation.  We will be working toward developing navigation systems that target the new Lunar Electric Rover and the Mark III space suits.

TECHNICAL ABSTRACT
The key innovation of this project will be the application of advanced Bayesian methods to integrate real-time dense stereo vision and high-speed optical flow with an Inertial Measurement Unit (IMU) to produce a highly accurate planetary rover navigation system. The software developed in this project will leverage current computing technology to implement advanced Visual Odometry (VO) methods that will accurately track much faster rover movements. Our fully Bayesian approach to VO will utilize more information from the images than previous methods are capable of using. Our Bayesian VO does not explicitly select features to track. Instead it implicitly determines what can be learned from each image pixel and weights the information accordingly. This means that our approach can work with images that have no distinct corners, which can be a significant advantage with low contrast images from permanently shadowed areas. We expect that the error characteristics of the visual processing with be complementary to the error characteristics of a low-cost IMU. Therefore, the combination of the two should provide highly accurate navigation.

POTENTIAL NASA COMMERCIAL APPLICATIONS
Visual Odometry (VO) has played a key role in Mars exploration with the Spirit and Opportunity Mars Exploration Rovers (MERs). However, limitations in onboard computing power severely limit the speed of movement that can be tracked by MERS VO, requiring an order of magnitude reduction in forward progress in area where VO was required.
The software developed in this project will leverage current computing technology to implement advanced VO methods that will accurately track much faster rover movements. This will greatly increase exploration productivity. This improvement will become even more significant when exploring the more distant planetary bodies.

This project will also investigate whether combining vision with a low-cost, lightweight, low-power Micro-ElectroMechanical System (MEMS) Inertial Measurement Unit (IMU) can produce acceptable accuracy for lunar and planetary exploration. If so, this will facilitate the design of lower-cost, light-weight rovers, which will make it feasible to launch a team of rovers for wide area exploration.

POTENTIAL NON-NASA COMMERCIAL APPLICATIONS
There will be many potential terrestrial applications for a Bayesian VO system. Although GPS-IMU systems can work well in open outdoor settings, GPS is degraded or unavailable in indoor settings or in outdoor areas with significant tree cover. A navigation system combining a GPS and an IMU with Bayesian VO could provide continuous operation in all environments. The success of this project should lay the groundwork for low-cost, low-power, light-weight integrated navigation systems for robots and autonomous vehicles operating in a wide range of environments.
One potential market for this technology is the Department of Defense (DoD). Congress has given DoD a mandate that by 2020 30% of ground vehicles should be robotic. An accurate, low-cost VO system should allow many of these vehicles to be semi-autonomous, enabling only supervisory control for many missions.

NASA’s technology taxonomy has been developed by the SBIR-STTR program to disseminate awareness of proposed and awarded R/R&D in the agency. It is a listing of over 100 technologies, sorted into broad categories, of interest to NASA.

TECHNOLOGY TAXONOMY MAPPING
Guidance, Navigation, and Control
Perception/Sensing

Bayesian Machine Intelligence Session at CIP 2010

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Kevin Knuth (President, Autonomous Exploration, Inc.) together with Ercan Kuruoglu is organizing a Bayesian Machine Intelligence Session at the 2nd International Workshop on Cognitive Information Processing (CIP2010) to be held from 14-15 June, 2010 on Elba Island (Tuscany), Italy.

This session will feature several prominent researchers involved with autonomous or cognitive robotics.
This includes, but is not limited to:

Pierre Bessière, CNRS, Grenoble University
Stephen Roberts, Oxford University, UK
Richard Dearden, University of Birmingham, UK

This special session will focus on the application of Bayesian probability theory to a variety of practical machine learning problems. Bayesian methods provide a unique opportunity to accommodate detailed information about the signal models as well as prior information about problem-specific constraints on parameter values. The recent increase in computational power as well as the development of more powerful sampling techniques have made the application of Bayesian methods to complex problems more practical than ever. This session will focus on a variety of signal processing applications as well as machine intelligence with a focus on robotics, intelligent instrumentation, experimental design, image understanding and vehicle health monitoring. There will be a live demo of a robot employing Bayesian adaptive exploration to solve a search-and-characterize problem.

The Foundations of Inference

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In October 2009, Kevin Knuth (President, Autonomous Exploration, Inc.) was invited to present a talk titled “Foundations of Inference” at the Perimeter Institute in Waterloo, CANADA.  This theoretical talk discussed the mathematical foundation of inference and its relation to order theory and quantum mechanics.  Due to the Quantum to Cosmos Festival which ran at the same time, this talk was not videotaped.

An abstract follows below:

Foundations of Inference

Kevin H. Knuth
Associate Professor of Physics and Informatics, University at Albany
President, Autonomous Exploration, Inc.

In our mutual desire to develop an intuitive understanding of quantum mechanics, John Skilling and I have been working to clarify the foundations of inference.

In this talk I introduce lattice theory as a new foundation for a rational description of the world around us.  I will show that fundamental mathematical fields such as measure theory, probability theory and information theory are based on basic symmetries of the lattice and can be easily derived.

Our rational description relies on a partially ordered set (poset) of states.  The process of expansion (lattice exponentiation) generates a distributive lattice from a poset.  I show that successive expansions applied to the poset of states results in a lattice of all the possible statements that can be made about the system, and a lattice of all the possible questions that can be asked of a system.

Quantification is performed by introducing real-valued functions called valuations.  The symmetries of the lattice lead to a unique calculus that obeys a summation property, which is the basis of addition as the fundamental operation in measure theory.  Introduction of the notion of context via functions called bi-valuations leads to a product rule for context-based measures.  On the lattice of statements, this unique calculus is the probability calculus with its sum and product rule.  An additional constraint relating the relevance of a question to the probabilities of the statements that answer it leads to entropy as the basic measure of a question.  The unique calculus on the lattice of questions is a new context-based generalization of information theory.

I will finish by introducing a new picture of quantum mechanics where experimental states are partially ordered to form a poset.  The valuations on this poset are the quantum amplitudes with Feynman path integrals representing the summation property.  Expansion of the state poset to a lattice of statements leads to probabilities whose values depend on the quantum amplitudes.  In this way probability theory naturally enters the quantum picture as inference.