A Comparative Study of Probabilistic Roadmap Planners
By Roland Geraerts, Mark H. Overmars. Workshop on the Algorithmic Foundations of Robotics, 2002
339 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Automotive Engineering, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/7d8f72f75f8fe06ac4e7048155bf7db40d680612
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
- P. Koopman
- David Hsu
- Jean-Claude Latombe
- Michael D. Wagner
- Hanna Kurniawati
- Emilio Frazzoli
- Steven M. LaValle
- Mitul Saha
In Automotive Engineering
- P. Koopman
- David Hsu
- Jean-Claude Latombe
- Michael D. Wagner
- Hanna Kurniawati
- Rasheed Hussain
- Sherali Zeadally
In Computer Vision and Pattern Recognition
- David Hsu
- P. Koopman
- Jean-Claude Latombe
- Michael D. Wagner
- Hanna Kurniawati
- Owen Macindoe
- Emilio Frazzoli
- James Harrison
Citations in the corpus
- Challenges in Autonomous Vehicle Testing and Validation (2016, neutral): “, probabilistic roadmap planners [18]).”
- Autonomous Vehicle Safety: An Interdisciplinary Challenge (2017, background): “What really matters is that the machine learning validation set has to be comprehensive enough to make sure that there are no gaps in system behavior.”
- On the Probabilistic Foundations of Probabilistic Roadmap Planning (2006, methodology): “Several systematic experimental studies have also compared various PRM planners, in terms of their sampling and connection strategies (e.g., Geraerts and Overmars 2002, 2005b; Morales A., Pearce, and Amato 2006).”