The bridge test for sampling narrow passages with probabilistic roadmap planners
By David Hsu, Tingting Jiang, J. Reif, Zheng Sun. IEEE International Conference on Robotics and Automation, 2003
429 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/0aa32e81c57c3f67e772f0a68169264c15f39d90
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Emilio Frazzoli
- Yoshiaki Kuwata
- Jonathan P. How
- David Hsu
- Samuel Rodríguez
- Nancy M. Amato
- Jyh-Ming Lien
- Jean-Claude Latombe
In Computer Vision and Pattern Recognition
- Emilio Frazzoli
- Jonathan P. How
- Yoshiaki Kuwata
- David Hsu
- Justin Teo
- Gaston A. Fiore
- Samuel Rodríguez
- Nancy M. Amato
In Control and Systems Engineering
- Emilio Frazzoli
- David Hsu
- Jean-Claude Latombe
- Jonathan P. How
- Hanna Kurniawati
- Steven M. LaValle
- Sertac Karaman
- Jur van den Berg
Citations in the corpus
- On the Probabilistic Foundations of Probabilistic Roadmap Planning (2006, background): “The local geometric pattern captured by the three configurationsq, q ′, andqm suggests strongly that qm lies in a narrow passage (Hsu et al. 2003).”
- Motion planning for urban driving using RRT (2008, methodology): “The first extension to the RRT algorithm in [10] is that it uses the physical and logical structure of the environment to bias the sampling [11].”
- An obstacle-based rapidly-exploring random tree (2006, methodology): “A number of methods specifically targeted at this problem have been proposed, e.g., [1], [7], [ 9 ], [10], [22].”