Syllabus | 16735

RI 16-735
Robotic Motion Planning Reading Assignment
Howie Choset

You are required to read one or two research papers with your group members and present them in the class. In the beginning of the semester, research papers will be selected based on students' interests and relevance to the course topics. You learn general ideas and fundamental details of the week's topic and will present corresponding papers in the following week. (see below or Syllabus)
Not your work
Not your officemate's work
Maybe a CMU research
Wes Huang's How To Read a Research Paper
Grading Sheet (Speaker brings enough copies to class)
Students' interests

Dille, Michael : Navigation/Dynamics-informed planning/Probabilistic localization (Kalman)
Edelson, Michael : Mapping/Coverage (Map/Coverage - solo)
Gentilini, Iacopo : Locomotion (Potential function with dynamics paper)
Hatton, Ross : Locomotion (Map for dynamic vehicles)
Hyde, Sean : Household robotics (D* Lite)
Knepper, Ross : High-speed planning/OA for nonholonomic vehicles (D* Lite)
Lipchin, Boris : Probabilistic robotics (Bayes)
Nagarajan, Umashankar : Kalman filtering (Kalman)
Park, HyunSoo : Locomotion (Potential function with dynamics paper)
Sanan, Siddharth : Motion planning/Optimal planning (Map for dynamic vehicles)

Schedule

Topic	Date	Paper	Presenting Group	Slides
Potential function with dynamics	9/19 Wed	J. Barraquand, B. Langlois, and J.C. Latombe, "Numerical potential field techniques for robot path planning," IEEE Transactions on Systems, Man and Cybernetics, Vol. 22, No. 2, 1992, pp. 224~241	Park H., Gentilini I.	Link
D*lite	10/1 Mon	G. A. Mills-Tettey, A. Stentz M., and B. Dias, "DD* Lite: Efficient Incremental Search with State Dominance," CMU-RI Technical Report (CMU-RI-TR-07-12), May, 2007	Hyde S., Knepper R.	Link
Map/Coverage	12/5 Wed	Y. Liu, R. Emery, D. Chakrabarti, W. Burgard, and S. Thrun. "Using EM to learn 3D models with mobile robots," In Proceedings of the International Conference on Machine Learning (ICML), 2001. D. Hähnel, W. Burgard, and S. Thrun, "Learning compact 3D models of indoor and outdoor environments with a mobile robot," Robotics and Autonomous Systems, 44:15-17, 2003.	Edelson M.	Link
Map for dynamic vehicles	11/7 Wed	R. Pepy, A. Lambert, and H. Mounier, "Path Planning using a Dynamic Vehicle Model," Information and Communication Technologies," Vol. 1, pp. 781- 786, 2006 F. Lamiraux and J.-P. Laumond, "Smooth Motion Planning for Car-Like Vehicles," IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION, VOL. 17, NO. 4, AUGUST 2001	Sanan S., Hatton R.	Link
Sampling based or Kalman filter	11/19 Mon	J. Djugash, S. Singh, G. Kantor, and W. Zhang, "Range-only SLAM for robots operating cooperatively with sensor networks," In proceedings of 2006 IEEE International Conference on Robotics and Automation, Orlando, FL, May 2006	Dille M., Nagarajan U.	Link
Bayesian framework	11/28 Wed	Arnaud Doucet, Nando de Freitas, Kevin Murphy, and Stuart Russell, "Rao-Blackwellised particle filtering for dynamic Bayesian networks," In Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, pages 176-- 183, Stanford, 2000. Doucet, A. and Godsill, "On Sequential Monte Carlo Sampling Methods for Bayesian Filtering," Available as Technical Report CUED/F-INFENG/TR. 310, Cambridge University Department of Engineering W. Fong and S. Godsill, "Sequential Monte Carlo simulation of dynamical models with slowly varying parameters: Application to audio," In Proceedings of the IEEE ICASSP, 2002 Jun S. Liu and Rong Chen, "Sequential Monte Carlo Methods for Dynamic Systems", Journal of the American Statistical Association, Vol. 93 (443), pp.1032--1044, 1998	Lipchin B.	Link

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Last Updated December 7, 2007