On the Probabilistic Foundations of Probabilistic Roadmap Planning

By David Hsu, Jean-Claude Latombe, Hanna Kurniawati. Int. J. Robotics Res., 2006

306 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/b204aa3ed1d6a8dbe8f9d856b9ef7bb5e46dc34e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. M. Simic
2. Mohamed Elbanhawi
3. Sertac Karaman
4. Emilio Frazzoli
5. Brian Ichter
6. Marco Pavone
7. James Harrison
8. Mark Moll

In Industrial and Manufacturing Engineering

1. Mark Moll
2. Emilio Frazzoli
3. Lydia E. Kavraki
4. Ioan Alexandru Sucan

In Mechanical Engineering

1. Mark Moll
2. Marco Pavone
3. Emilio Frazzoli
4. Lydia E. Kavraki
5. Brian Ichter
6. Ioan Alexandru Sucan
7. L. Wang

Citations in the corpus

• Fast Marching Tree: a Fast Marching Sampling-Based Method for Optimal Motion Planning in Many Dimensions (2013, unknown): “The Alpha puzzle presents ‘narrow corridors’ in X free (Amato et al., 1998; Hsu et al., 2006).”
• Path Planning for the Mobile Robot: A Review (2018, background): “The smoothness S [15] is defined as where, α and β are weighted coefficients, DA l is the number of angle of deflection larger than the desired variable, N f is the total number of path segments, S min is the number of segments with the smallest number of path segments in the path.”
• UAV-Assisted Data Collection for Internet of Things: A Survey (2022, background): “points and collision-free straight-line edges [76]–[78], as shown in Fig.”
• Sampling-based algorithms for optimal motion planning (2011, background): “More recently, the empirical success of sampling-based algorithms was argued to be strongly tied to the hypothesis that most practical robotic applications, even though involving robots with many degrees of freedom, feature environments with such good visibility properties (Hsu et al. 2006).”
• Learning Sampling Distributions for Robot Motion Planning (2017, background): “However, compared to the sampling distribution, the impact of the sample source is limited [2].”
• Heuristic approaches in robot path planning: A survey (2016, unknown): “Several improvements are proposed by [28,29].”
• Sampling-Based Robot Motion Planning: A Review (2014, background): “It was shown that PRM is probabilistically complete [58], [59].”
• The Open Motion Planning Library (2012, methodology): “Previous work has shown that the strategy used for sampling valid states in the state space significantly influences runtime of many planning algorithms [34].”