Award Abstract # 0931686
CPS:Medium: Tightly Integrated Perception and Planning in Intelligent Robotics

NSF Org: CNS
Division Of Computer and Network Systems
Recipient: CORNELL UNIVERSITY
Initial Amendment Date: August 15, 2009
Latest Amendment Date: August 10, 2011
Award Number: 0931686
Award Instrument: Standard Grant
Program Manager: Sylvia Spengler
sspengle@nsf.gov
 (703)292-7347
CNS
 Division Of Computer and Network Systems
CSE
 Direct For Computer & Info Scie & Enginr
Start Date: September 1, 2009
End Date: August 31, 2013 (Estimated)
Total Intended Award Amount: $1,473,121.00
Total Awarded Amount to Date: $1,473,121.00
Funds Obligated to Date: FY 2009 = $1,473,121.00
ARRA Amount: $1,473,121.00
History of Investigator:
  • Mark Campbell (Principal Investigator)
    mc288@cornell.edu
  • Noah Snavely (Co-Principal Investigator)
  • Hadas Kress Gazit (Co-Principal Investigator)
  • Daniel Huttenlocher (Co-Principal Investigator)
Recipient Sponsored Research Office: Cornell University
341 PINE TREE RD
ITHACA
NY  US  14850-2820
(607)255-5014
Sponsor Congressional District: 19
Primary Place of Performance: Cornell University
341 PINE TREE RD
ITHACA
NY  US  14850-2820
Primary Place of Performance
Congressional District:
19
Unique Entity Identifier (UEI): G56PUALJ3KT5
Parent UEI:
NSF Program(s): CPS-Cyber-Physical Systems
Primary Program Source: 01R00910DB RRA RECOVERY ACT
Program Reference Code(s): 6890, 7924, 9216, 9218, HPCC
Program Element Code(s): 7918
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.082

ABSTRACT

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).

The objective of this research is to develop truly intelligent, automated driving through a new paradigm that tightly integrates probabilistic perception and deterministic planning in a formal, verifiable framework. The interdisciplinary approach utilizes three interlinked tasks. Representations develops new techniques for constructing and maintaining representations of a dynamic environment to facilitate higher-level planning. Anticipation and Motion Planning develops methods to anticipate changes in the environment and use them as part of the planning process. Verifiable Task Planning develops theory and techniques for providing probabilistic guarantees for high-level behaviors. Ingrained in the approach is the synergy between theory and experiment using an in house, fully equipped vehicle.
The recent Urban Challenge showed the current brittleness of autonomous driving, where small perception mistakes would propagate into planners, causing near misses and small accidents; Fundamentally, there is a mismatch between probabilistic perception and deterministic planning, leading to "reactive" rather than "intelligent" behaviors. The proposed research directly addresses this by developing a single, unified theory of perception and planning for intelligent cyber-physical systems.
Near term, this research could be used to develop advanced safety systems in cars. The elderly and physically impaired would benefit from inexpensive, advanced automation in cars. Far term, the advanced intelligence could lead to automated vehicles for applications such as cooperative search and rescue. The research program will educate students through interdisciplinary courses in computer science and mechanical engineering, and experiential learning projects. Results will be disseminated to the community including under-represented colleges and universities.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Probabilistic Multi-Level Maps from LIDAR Data "C. Rivadeneyra, M. Campbell" International Journal of Robotics Research , 2011
M. Campbell "Intelligent Autonomy in Robotic Systems" The Bridge, Quarterly of the National Academy of Engineering , 2010
M. Campbell, M. Egerstedt, J. How, R. Murray "Autonomous Driving in Urban Environments: Approaches, Lessons and Challenges" Philosophical Transactions of the Royal Society - A , 2010
C. Rivadeneyra, M. Campbell "Probabilistic Multi-Level Maps from LIDAR Data" International Journal of Robotics Research , v.30(12) , 2011 , p.150
M. Campbell, M. Egerstedt, J. How, R. Murray "Autonomous Driving in Urban Environments: Approaches, Lessons and Challenges" Philosophical Transactions of the Royal Society - A , v.368 , 2010 , p.46
Isaac Miller and Mark Campbell "Sensitivity Study of a Tightly-Coupled GPS / INS System for Autonomous Navigation" IEEE Transactions on Aerospace and Electronic Systems , v.48(2) , 2012 , p.1115-1135
Isaac Miller, Mark Campbell, Dan Huttenlocher "Efficient Unbiased Tracking of Multiple Dynamic Obstacles Under Large Viewpoint Changes" IEEE Transactions on Robotics , v.27(1) , 2011 , p.29-46

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