Evaluating trajectory collision probability through adaptive importance sampling for safe motion planning

E Schmerling, M Pavone - arXiv preprint arXiv:1609.05399, 2016 - arxiv.org
This paper presents a tool for addressing a key component in many algorithms for planning
robot trajectories under uncertainty: evaluation of the safety of a robot whose actions are …
被引用次数：48 相关文章
[PDF] roboticsproceedings.org

[PDF][PDF] Evaluating Trajectory Collision Probability through Adaptive Importance Sampling for Safe Motion Planning

E Schmerling, M Pavone - m.roboticsproceedings.org
This paper presents a tool for addressing a key component in many algorithms for planning
robot trajectories under uncertainty: evaluation of the safety of a robot whose actions are …
[PDF] roboticsproceedings.org

[PDF][PDF] Evaluating Trajectory Collision Probability through Adaptive Importance Sampling for Safe Motion Planning

E Schmerling, M Pavone - roboticsproceedings.org
This paper presents a tool for addressing a key component in many algorithms for planning
robot trajectories under uncertainty: evaluation of the safety of a robot whose actions are …

Evaluating Trajectory Collision Probability through Adaptive Importance Sampling for Safe Motion Planning

E Schmerling, M Pavone - arXiv e-prints, 2016 - ui.adsabs.harvard.edu
This paper presents a tool for addressing a key component in many algorithms for planning
robot trajectories under uncertainty: evaluation of the safety of a robot whose actions are …
[PDF] roboticsproceedings.org

[PDF][PDF] Evaluating Trajectory Collision Probability through Adaptive Importance Sampling for Safe Motion Planning

E Schmerling, M Pavone - roboticsproceedings.org
This paper presents a tool for addressing a key component in many algorithms for planning
robot trajectories under uncertainty: evaluation of the safety of a robot whose actions are …
[PDF] github.io

[PDF][PDF] Evaluating Trajectory Collision Probability through Adaptive Importance Sampling for Safe Motion Planning

E Schmerling, M Pavone - stanfordasl.github.io
This paper presents a tool for addressing a key component in many algorithms for planning
robot trajectories under uncertainty: evaluation of the safety of a robot whose actions are …