NASA STTR 2010 Solicitation


PROPOSAL NUMBER: 10-1 T1.02-9834
RESEARCH SUBTOPIC TITLE: Information Technologies for Intelligent Planetary Robotics
PROPOSAL TITLE: Simultaneous Localization and Mapping for Planetary Surface Mobility

NAME: ProtoInnovations, LLC NAME: Carnegie Mellon University
STREET: 1908 Shaw Avenue STREET: 5000 Forbes Ave.
CITY: Pittsburgh CITY: Pittsburgh
STATE/ZIP: PA  15217 - 1710 STATE/ZIP: PA  15213 - 3890
PHONE: (412) 916-8807 PHONE: (412) 268-5421

PRINCIPAL INVESTIGATOR/PROJECT MANAGER (Name, E-mail, Mail Address, City/State/Zip, Phone)
David S Wettergreen
5000 Forbes Ave.
Pittsburgh, PA 15213 - 3890
(412) 268-5421

Estimated Technology Readiness Level (TRL) at beginning and end of contract:
Begin: 3
End: 4

TECHNICAL ABSTRACT (Limit 2000 characters, approximately 200 words)
ProtoInnovations, LLC and Carnegie Mellon University have formed a partnership to commercially develop localization and mapping technologies for planetary rovers. Our first aim is to provide a reliable means of localization that is independent of infrastructure, such as GPS, and compatible with requirements of missions to planetary surfaces. Simultaneously solving for the precise location of the rover as it moves while building an accurate map of the environment is an optimization problem involving internal sensing, sensing of the surrounding environment, probabilistic optimization methods, efficient data structures, and a robust implementation. Our second aim is to merge simultaneous localization and mapping (SLAM) technologies with our existing Reliable Autonomous Surface Mobility (RASM) architecture for rover navigation. Our unique partnership brings together state-of-the-art technologies for SLAM with experience in delivering and supporting both autonomous systems and mobility platforms for NASA.

Our proposed project will create a SLAM framework that is capable of accurately localizing a rover throughout long, multi-kilometer traverses of barren terrain. Our approach is compatible with limited communication and computing resources expected for missions to planetary surfaces. Our technology is based on innovative representations of evidence grids, particle-filter algorithms that operate on range data rather than explicit features, and strategies for segmenting large evidence grids into manageable pieces.

In this project we will evaluate the maturity of these algorithms, developed for research programs at Carnegie Mellon, and incorporate them into our RASM architecture, thus providing portable and reliable localization for a variety of vehicle platforms and sensors. Mission constraints will vary broadly, so our SLAM components will be able to merge readings from various suites of sensors that may be found on planetary rovers.

POTENTIAL NASA COMMERCIAL APPLICATIONS (Limit 1500 characters, approximately 150 words)
Technical innovations from this project will have immediate application to the Lunar All-Terrain Utility Vehicle (LATUV) and will enable it to achieve its full capability as it becomes a research appliance for NASA. We also see direct applicability of this work to vehicles developed by NASA for scientific exploration, habitat construction, and landing site preparation. ProtoInnovations will seek to sustain this work by providing SLAM capability to a wide range of planetary rover vehicles and prototypes.

POTENTIAL NON-NASA COMMERCIAL APPLICATIONS (Limit 1500 characters, approximately 150 words)
We believe that our effort can be sustained by our unique capability and experience, which we believe is valuable to emerging lunar-rover activities in Canada, Japan, and India / Russia. The market for lunar-rover autonomy is not large but it is highly technical and critical to mission success. ProtoInnovations intends to continue R&D to position itself as a world leader in rover navigation software and experience.

Commercially-available, high-resolution positioning systems have been maturing rapidly over the past decade and have penetrated into agricultural, mining, and defense markets. Most of these products filter GPS data with measurements from IMUs, encoders, and so on. Such products are robust to brief GPS drop-outs but are not robust to long-term GPS losses. Such losses are frequent in urban areas or in dense foliage, and should be expected indoors and when GPS signals are jammed. When GPS is lost, inertial measurements and dead-reckoning drift and the resulting localization error increases over time and distance traveled. SLAM is an ideal way of correcting these errors without the need for infrastructure. Our SLAM approach, with its computational efficiency and loose requirements on operating environment, is a feasible add-on technology for commercially available positioning systems.

TECHNOLOGY TAXONOMY MAPPING (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.)
Autonomous Control (see also Control & Monitoring)
Robotics (see also Control & Monitoring; Sensors)

Form Generated on 09-03-10 15:17