Papers on Sampling-Based Motion Planning
Shortcutting revisited: Improved sampling and irreplaceable optimality. E. F. Awad, H. Erickson, A. J. LaValle, N. Prencipe, B. Sakcak, K. G. Timperi, and S. M. LaValle. In S. Caron, S. M. LaValle, T. Marcucci, B. Sakcak, and O. Salzman, editors, Algorithmic Foundations of Robotics, XVII. Springer-Verlag, Berlin, 2027. [pdf].
Universal plans: One action sequence to solve them all!. K. G. Timperi, A. J. LaValle, and S. M. LaValle. In N. Amato, K. Driggs-Campbell, E. Chinwe, M. Morales, and J. M. O'Kane, editors, Algorithmic Foundations of Robotics, XVI. Springer-Verlag, Berlin, 2025. [pdf].
Bang-bang boosting of RRTs. A. J. LaValle, B. Sakcak, and S. M. LaValle. In IEEE International Conference on Intelligent Robots and Systems, pages 2869-2876, 2023. [pdf].
Human perception-optimized planning for comfortable VR-based telepresence. I. Becerra, M. Suomalainen, E. Lozano, K. J. Mimnaugh, R. Murrieta-Cid, and S. M. LaValle. IEEE Robotics and Automation Letters, 5(4):6489-6496, 2020. [pdf].
Continuous planning with winding constraints using optimal heuristic-driven front propagation. D. S. Yershov, P. Vernaza, and S. M. LaValle. In IEEE International Conference on Robotics and Automation, 2013. [pdf].
Simplicial label correcting algorithms for continuous stochastic shortest path problems. D. S. Yershov and S. M. LaValle. In IEEE International Conference on Robotics and Automation, 2013. [pdf].
Simplicial Dijkstra and A* algorithms for optimal feedback planning. D. Yershov and S. M. LaValle. Advanced Robotics, 26(17):2065-2085, 2012. [pdf].
Simplicial Dijkstra and A* algorithms for optimal feedback planning. D. Yershov and S. M. LaValle. In Proceedings IEEE International Conference on Intelligent Robots and Systems, 2011. [pdf].
Space-filling trees: A new perspective on motion planning via incremental search. J. Kuffner and S. M. LaValle. In Proceedings IEEE International Conference on Intelligent Robots and Systems, 2011. [pdf].
Motion planning: The essentials. S. M. LaValle. IEEE Robotics and Automation Society Magazine, 18(1):79-89, 2011. [pdf].
Motion planning: Wild frontiers. S. M. LaValle. IEEE Robotics and Automation Society Magazine, 18(2):108-118, 2011. [pdf].
Sufficient conditions for the existence of resolution complete planning algorithms. D. Yershov and S. M. LaValle. In Proceedings Workshop on Algorithmic Foundations of Robotics (WAFR), 2010. [pdf].
Generating uniform incremental grids on SO(3) using the Hopf fibration. A. Yershova, S. Jain, S. M. LaValle, and J. C. Mitchell. International Journal of Robotics Research, 29(7), 2010. [pdf].
Survivability: Measuring and ensuring path diversity. L. H. Erickson and S. M. LaValle. In Proceedings IEEE International Conference on Robotics and Automation, 2009. [pdf].
Motion planning. L. E. Kavraki and S. M. LaValle. In B. Siciliano and O. Khatib, editors, Springer Handbook of Robotics. Springer-Verlag, 2008. [pdf].
Motion planning for highly constrained spaces. A. Yershova and S. M. LaValle. Technical Report UIUCDCS-R-2008-2975, Department of Computer Science, University of Illinois, 2008. [pdf].
Generating uniform incremental grids on SO(3) using the Hopf fibration. A. Yershova, S. M. LaValle, and J. C. Mitchell. In Proceedings Workshop on Algorithmic Foundations of Robotics (WAFR), 2008. [pdf].
Improving the performance of sampling-based motion planning with symmetry-based gap reduction. P. Cheng, E. Frazzoli, and S. M. LaValle. IEEE Transactions on Robotics, 24(2):488-494, April 2008. [pdf].
Improving motion planning algorithms by efficient nearest-neighbor searching. A. Yershova and S. M. LaValle. IEEE Transactions on Robotics, 23(1):151-157, February 2007. [pdf].
A multiresolution approach for motion planning under differential constraints. S. R. Lindemann and S. M. LaValle. In Proceedings IEEE International Conference on Robotics and Automation, 2006. [pdf].
Chapter 5: Sampling-Based Motion Planning, Planning Algorithms. S. M. LaValle. Cambridge University Press, Cambridge, U.K., 2006. [pdf] [Entire Book].
Chapter 14: Sampling-Based Planning Under Differential Constraints, Planning Algorithms. S. M. LaValle. Cambridge University Press, Cambridge, U.K., 2006. [pdf] [Entire Book].
Adaptive tuning of the sampling domain for dynamic-domain RRTs. L. Jaillet, A. Yershova, S. M. LaValle, and T. Simeon. In Proceedings IEEE International Conference on Intelligent Robots and Systems, 2005. [pdf].
Dynamic-domain RRTs: Efficient exploration by controlling the sampling domain. A. Yershova, L. Jaillet, T. Simeon, and S. M. LaValle. In Proceedings IEEE International Conference on Robotics and Automation, 2005. [pdf].
On the relationship between classical grid search and probabilistic roadmaps. S. M. LaValle, M. S. Branicky, and S. R. Lindemann. International Journal of Robotics Research, 23(7/8):673-692, July/August 2004. [pdf].
Steps toward derandomizing RRTs. S. R. Lindemann and S. M. LaValle. In IEEE Fourth International Workshop on Robot Motion and Control, 2004. [pdf].
Improving the performance of sampling-based planners by using a symmetry-exploiting gap reduction algorithm. P. Cheng, E. Frazzoli, and S. M. LaValle. In Proceedings IEEE International Conference on Robotics and Automation, 2004. [pdf].
Deterministic sampling methods for spheres and SO(3). A. Yershova and S. M. LaValle. In Proceedings IEEE International Conference on Robotics and Automation, 2004. [pdf].
Incrementally reducing dispersion by increasing Voronoi bias in RRTs. S. R. Lindemann and S. M. LaValle. In Proceedings IEEE International Conference on Robotics and Automation, 2004. [pdf].
The sampling-based neighborhood graph: A framework for planning and executing feedback motion strategies. L. Yang and S. M. LaValle. IEEE Transactions on Robotics and Automation, 20(3):419-432, June 2004. [pdf].
Current issues in sampling-based motion planning. S. R. Lindemann and S. M. LaValle. In P. Dario and R. Chatila, editors, Robotics Research: The Eleventh International Symposium, pages 36-54. Springer-Verlag, Berlin, 2005. [pdf].
Incremental low-discrepancy lattice methods for motion planning. S. R. Lindemann and S. M. LaValle. In Proceedings IEEE International Conference on Robotics and Automation, pages 2920-2927, 2003. [pdf].
On the relationship between classical grid search and probabilistic roadmaps. S. M. LaValle and M. S. Branicky. In J.-D. Boissonat, J. Burdick, K. Y. Goldberg, and S. A. Hutchinson, editors, Algorithmic Foundations of Robotics. Springer-Verlag, Berlin, 2003. [pdf].
From dynamic programming to RRTs: Algorithmic design of feasible trajectories. S. M. LaValle. In A. Bicchi, H. I. Christensen, and D. Prattichizzo, editors, Control Problems in Robotics, pages 19-37. Springer-Verlag, Berlin, 2002. [pdf].
Deterministic vs. probabilistic roadmaps. M. S. Branicky, S. M. LaValle, K. Olson, and L. Yang. Unpublished manuscript, 2002, [pdf].
Pointers to quasi-monte carlo literature. S. M. LaValle. University of Illinois, December 2002, [pdf].
Efficient nearest neighbor searching for motion planning. A. Atramentov and S. M. LaValle. In Proceedings IEEE International Conference on Robotics and Automation, pages 632-637, 2002. [pdf].
Resolution complete rapidly-exploring random trees. P. Cheng and S. M. LaValle. In Proceedings IEEE International Conference on Robotics and Automation, pages 267-272, 2002. [pdf].
An improved random neighborhood graph approach. L. Yang and S. M. LaValle. In Proceedings IEEE International Conference on Robotics and Automation, pages 254-259, 2002. [pdf].
Randomized kinodynamic planning. S. M. LaValle and J. J. Kuffner. The International Journal of Robotics Research, 20(5):378-400, May 2001. [pdf].
RRT-based trajectory design for autonomous automobiles and spacecraft. P. Cheng, Z. Shen, and S. M. LaValle. Archives of Control Sciences, 11(3-4):167-194, 2001. [pdf].
Reducing metric sensitivity in randomized trajectory design. P. Cheng and S. M. LaValle. In Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 43-48, 2001. [pdf].
Quasi-randomized path planning. M. S. Branicky, S. M. LaValle, K. Olson, and L. Yang. In Proceedings IEEE International Conference on Robotics and Automation, pages 1481-1487, 2001. [pdf].
Randomized path planning for linkages with closed kinematic chains. J. Yakey, S. M. LaValle, and L. E. Kavraki. IEEE Transactions on Robotics and Automation, 17(6):951-958, December 2001. [pdf].
Rapidly-exploring random trees: Progress and prospects. S. M. LaValle and J. J. Kuffner. In B. R. Donald, K. M. Lynch, and D. Rus, editors, Algorithmic and Computational Robotics: New Directions, pages 293-308. A K Peters, Wellesley, MA, 2001. [pdf].
Using randomization to find and optimize feasible trajectories for nonlinear systems. P. Cheng, Z. Shen, and S. M. LaValle. In Proceedings Annual Allerton Conference on Communications, Control, Computing, pages 926-935, 2000. [pdf].
RRT-connect: An efficient approach to single-query path planning. J. J. Kuffner and S. M. LaValle. In Proceedings IEEE International Conference on Robotics and Automation, pages 995-1001, 2000. [pdf].
A framework for planning feedback motion strategies based on a random neighborhood graph. L. Yang and S. M. LaValle. In Proceedings IEEE International Conference on Robotics and Automation, pages 544-549, 2000. [pdf].
Randomized kinodynamic planning. S. M. LaValle and J. J. Kuffner. In Proceedings IEEE International Conference on Robotics and Automation, pages 473-479, 1999. [pdf].
A probabilistic roadmap approach for systems with closed kinematic chains. S. M. LaValle, J. Yakey, and L. E. Kavraki. In Proceedings IEEE International Conference on Robotics and Automation, pages 1671-1676, 1999. [pdf].
Rapidly-exploring random trees: A new tool for path planning. S. M. LaValle. TR 98-11, Computer Science Dept., Iowa State University, October 1998, [pdf].