A singly exponential stratification scheme for real semi-algebraic varieties and its applications
Theoretical Computer Science
Motion planning amidst fat obstacles (extended abstract)
SCG '94 Proceedings of the tenth annual symposium on Computational geometry
Randomized algorithms
Computing depth orders for fat objects and related problems
Computational Geometry: Theory and Applications
Randomized query processing in robot path planning
STOC '95 Proceedings of the twenty-seventh annual ACM symposium on Theory of computing
Range searching and point location among fat objects
Journal of Algorithms
Range searching in low-density environments
Information Processing Letters
Realistic input models for geometric algorithms
SCG '97 Proceedings of the thirteenth annual symposium on Computational geometry
A random sampling scheme for path planning
International Journal of Robotics Research
Handbook of discrete and computational geometry
Geometric pattern matching: a performance study
SCG '99 Proceedings of the fifteenth annual symposium on Computational geometry
The digital Michelangelo project: 3D scanning of large statues
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Robot Motion Planning
Smoothed analysis of the perceptron algorithm for linear programming
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Dynamic Motion Planning in Low Obstacle Density Environments
WADS '97 Proceedings of the 5th International Workshop on Algorithms and Data Structures
Linear Size Binary Space Partitions for Fat Objects
ESA '95 Proceedings of the Third Annual European Symposium on Algorithms
Pattern Matching for Spatial Point Sets
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time
Journal of the ACM (JACM)
Almost tight upper bounds for vertical decompositions in four dimensions
Journal of the ACM (JACM)
Improved Smoothed Analysis of the Shadow Vertex Simplex Method
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
On the Probabilistic Foundations of Probabilistic Roadmap Planning
International Journal of Robotics Research
Worst-case and Smoothed Analysis of the ICP Algorithm, with an Application to the k-means Method
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
Visibility maps of realistic terrains have linear smoothed complexity
Proceedings of the twenty-fifth annual symposium on Computational geometry
3D curves with a prescribed curvature and torsion for an aerial robot
International Journal of Computer Applications in Technology
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The probabilistic roadmap algorithm is a leading heuristic for robot motion planning. It is extremely efficient in practice, yet its worst case convergence time is unbounded as a function of the input's combinatorial complexity. We prove a smoothed polynomial upper bound on the number of samples required to produce an accurate probabilistic roadmap, and thus on the running time of the algorithm, in an environment of simplices. This sheds light on its widespread empirical success.