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| | ==Environment reconstruction== | | ==Environment reconstruction== |
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| − | =Publications Related to this project=
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| − | * B. Johnson. F. Havlak, M. Campbell. H. Kress-Gazit, “Execution and Analysis of High-Level Tasks with Dynamic Obstacle Anticipation,” 2012 International Conference on Robotics and Automation.
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| − | * J. Hardy, M. Campbell, “Clustering Obstacle Predictions to Improve Contingency Planning for Autonomous Road Vehicles in Congested Environments,” IEEE/RSJ International Conference on Intelligent Robots and Systems, Sept 2011.
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| − | * M. Campbell, “Intelligent Autonomy in Robotic Systems,” The Bridge, Quarterly of the National Academy of Engineering, Winter 2010-11, pp 27-34.
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| − | * C. Rivadeneyra, M. Campbell, “Probabilistic Multi-Level Maps from LIDAR Data,” International Journal of Robotics Research, published on-line Jan 2011, Vol. 30 No. 12, Oct 2011, pp 1508-1526.
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| − | * M. Campbell, M. Egerstedt, J. How, R. Murray, “Autonomous Driving in Urban Environments: Approaches, Lessons and Challenges,” Philosophical Transactions of the Royal Society - A, Vol 368, 2010, pp. 4649-4672
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| − | * J. Schoenberg, A. Nathan, M. Campbell, “Segmentation of Dense Range Information in Complex Urban Scenes,” IEEE/RSJ International Conference on Intelligent Robots and Systems, Oct 2010.
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| − | * I. Miller, M. Campbell, “Sensitivity Study of a Tightly-Coupled GPS / INS System for Autonomous Navigation,” IEEE Transactions on Aerospace and Electronic Systems, published on-line Dec 2011, Vol 48, No 2, April 2012, pp 1115-1135.
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| − | * I. Miller, M. Campbell, D. Huttenlocher, “Efficient Unbiased Tracking of Multiple Dynamic Obstacles Under Large Viewpoint Changes,” IEEE Transactions on Robotics, Vol 27, No 1, Feb 2011, pp 29-46.
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| − | * I. Miller, M. Campbell, D. Huttenlocher, et al, “Team Cornell's Skynet: Robust Perception and Planning in an Urban Environment,” Journal of Field Robotics, Vol 25, No 8, pp 493-527, 2008.
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Research goal of our NSF CPS project is to develop algorithms for intelligent robotics which tightly integrate probabilistic perception and deterministic planning in a verifiable framework. Current activities include: 1) Developing representations for constructing and maintaining models of complex environments; 2) Developing methods to anticipate changes in the environment and use them as part of planning; 3) Developing task planning methods that provide probabilistic guarantees for high-level behaviors. The educational goal is to educate students in interdisciplinary CS and engineering projects and courses, particularly those related to intelligent robotics.
