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Research Spending & Results

Award Detail

Awardee:CORNELL UNIVERSITY, INC
Doing Business As Name:Cornell University
PD/PI:
  • Mark E Campbell
  • (607) 255-4268
  • mc288@cornell.edu
Co-PD(s)/co-PI(s):
  • Hadas Kress Gazit
  • Noah Snavely
  • Daniel P Huttenlocher
Award Date:08/15/2009
Estimated Total Award Amount: $ 1,473,121
Funds Obligated to Date: $ 1,473,121
  • FY 2009=$1,473,121
Start Date:09/01/2009
End Date:08/31/2013
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040101 RRA RECOVERY ACT
Award Title or Description:CPS:Medium: Tightly Integrated Perception and Planning in Intelligent Robotics
Federal Award ID Number:0931686
DUNS ID:872612445
Parent DUNS ID:002254837
Program:CPS-Cyber-Physical Systems
Program Officer:
  • Sylvia Spengler
  • (703) 292-8930
  • sspengle@nsf.gov

Awardee Location

Street:373 Pine Tree Road
City:Ithaca
State:NY
ZIP:14850-2820
County:Ithaca
Country:US
Awardee Cong. District:23

Primary Place of Performance

Organization Name:Cornell University
Street:373 Pine Tree Road
City:Ithaca
State:NY
ZIP:14850-2820
County:Ithaca
Country:US
Cong. District:23

Abstract at Time of Award

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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C. Rivadeneyra, M. Campbell "Probabilistic Multi-Level Maps from LIDAR Data" International Journal of Robotics Research, v.30(12), 2011, p.150.

Probabilistic Multi-Level Maps from LIDAR Data "C. Rivadeneyra, M. Campbell" International Journal of Robotics Research, v., 2011, p..

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.

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.

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., 2010, p..

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.

M. Campbell "Intelligent Autonomy in Robotic Systems" The Bridge, Quarterly of the National Academy of Engineering, v., 2010, p..

M. Campbell "Intelligent Autonomy in Robotic Systems" The Bridge, Quarterly of the National Academy of Engineering, v., 2010, p..

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