Some relevant papers for "Motion Planning and Control Under Uncertainty"

 

Follow this link for a more complete list of relevant papers for the course and related topics

 

Below are links to full PDFs of some selected papers for the course:

 

[Basye92] Basye, K., et al., A Decision-Theoretic Approach to Planning, Perception, and Control. IEEE Expert-Intelligent Systems & Their Applications, 1992. 7(4): p. 58-65.

 

Bayesian inference in biological systems

[Knill04]  Knill, D.C. and A. Pouget, The Bayesian brain: the role of uncertainty in neural coding and computation. Trends in Neuroscience, 2004. 27(12): p. 712-719.

[Kording04]  Kording, K.P. and D.M. Wolpert, Bayesian integration in sensorimotor learning. Nature, 2004. 427(6971): p. 244-247.

[Paulin05a] Paulin, M.G., Evolutionary origins and principles of distributed neural computation for state estimation and movement control in vertebrates. Complexity, 2005. 10(3): p. 56-65.

[Paulin05b] Paulin, M.G., Evolution of the cerebellum as a neuronal machine for Bayesian state estimation. Journal of Neural Engineering, 2005. 2(3): p. S219-S234.

 

 

Distributed sensor networks

[Chung04] Chung, T.H., et al. On a decentralized active sensing strategy using mobile sensor platforms in a network. in Conference on Decision and Control. 2004.

[Freeman06] Freeman, R.A., P. Yang, and K.M. Lynch. Distributed estimation and control of swarm formation statistics. in American Control Conference. 2006.

[Grocholosky00] Grocholosky, B., H. Durrant-Whyte, and P. Gibbens. An information-theoretic approach to decentralized control of multiple autonomous flight vehicles. in Sensor Fusion and Decentralized Control in Robotic Systems III. 2000. Boston, MA, USA.

[Olfati-Saber05] Olfati-Saber, R. Distributed Kalman filter with embedded consensus filters. in 44th IEEE Conference on Decision and Control. 2005.

 

 

Fundamental papers on Bayesian and Kalman filtering

[Arulampalam02] Arulampalam, M.S., et al., A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 2002. 50(2): p. 174-188.

[Fox03]  Fox, D., et al., Bayesian filtering for location estimation. IEEE Pervasive Computing, 2003. 2(3): p. 24-33.

[Smith&Cheeseman86]  Smith, R.C. and P. Cheeseman, On the representation and estimation of spatial uncertainty. The International Journal of Robotics Research, 1986. 5(4): p. 56-68.

[Welch&Bishop]  Welch, G. and G. Bishop, An introduction to the Kalman filter. 2002, University of North Carolina: Chapel Hill, NC.

 

 

LQG applications in biological systems

[Kuo02]  Kuo, A.D., The relative roles of feedforward and feedback in the control of rhythmic movements. Motor Control, 2002. 6(2): p. 129-145.

[Todorov04]  Todorov, E., Optimality principles in sensorimotor control. Nature Neuroscience, 2004. 7(9): p. 907-915.

[Todorov02a]  Todorov, E. and M.I. Jordan, Optimal feedback control as a theory of motor coordination. Nature Neuroscience, 2002. 5(11): p. 1226-1235.

[Todorov02b]  Todorov, E. and M.I. Jordan, Optimal feedback control as a theory of motor coordination: Supplementary notes. Nature Neuroscience: supplement, 2002. 5(11): p. 1226-1235.

 

 

Model-based control

[Wolpert97]  Wolpert, D., Computational approaches to motor control. Trends in Cognitive Science, 1997. 1(6): p. 209-16.

 

 

POMDP

[Cassandra96] Cassandra, A.R., L.P. Kaelbling, and J.A. Kurien. Acting under uncertainty: Discrete Bayesian models for mobile robot navigation. in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 1996.

[Kaelbling98]  Kaelbling, L.P., M.L. Littman, and A.R. Cassandra, Planning and acting in partially observable stochastic domains. Artificial Intelligence, 1998. 101(1-2): p. 99-134.

Also see Tony Cassandra's "POMDP Page": http://www.cassandra.org/pomdp/ There are some great resources including a good tutorial.

 

 

Pursuer-evader

[Ghose06] Ghose, K., et al., Echolocating bats use a nearly time-optimal strategy to intercept prey. 2006, University of Maryland: College Park, MD.

[Kuc94] Kuc, R., Sensorimotor Model of Bat Echolocation and Prey Capture. Journal of the Acoustical Society of America, 1994. 96(4): p. 1965-1978.

[Patsko01] Patsko, V.S. and V.L. Turova, Homicidal Chauffeur Game: Computation of Level Sets of the Value Function. Annals - International Society of Dynamic Games, 2001. 6: p. 295-322.

[Lygeros99] Lygeros, J., C. Tomlin, and S. Sastry, Controllers for reachability specifications for hybrid systems. Automatica, 1999. 35(3): p. 349-370.

 

 

Sensing for uncertainty minimization (active sensing)

[Bay91]  Bay, J.S., A Fully Autonomous Active Sensor-Based Exploration Concept for Shape-Sensing Robots. IEEE Transactions on Systems Man and Cybernetics, 1991. 21(4): p. 850-860.

[Bourgault02] Bourgault, F., et al. Information based adaptive robotic exploration. in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2002). 2002. Lausanne, Switzerland.

[Feder99]  Feder, H.J.S., J.J. Leonard, and C.M. Smith, Adaptive mobile robot navigation and mapping. International Journal of Robotics Research, 1999. 18(7): p. 650-668.

[Mihaylova03]  Mihaylova, L., et al., Active Sensing for Robotics - A survey. 2003, Katholieke Universiteit Leuven, Department of Mechanical Engineering: Heverlee Belgium.

 



last modified: April 4, 2006 9:11