Abstract Partially observable Markov decision processes (POMDPs) are a principled mathematical framework for planning under uncertainty, a crucial capability for reliable operation...
Hanna Kurniawati, Yanzhu Du, David Hsu, Wee Sun Le...
— Classical path planning does not address many of the challenges of robotic systems subject to differential constraints. While there have been many recent efforts to develop mot...
We present an approach to path planning for humanoid robots that computes dynamically-stable, collision-free trajectories from full-body posture goals. Given a geometric model of t...
James J. Kuffner Jr., Satoshi Kagami, Koichi Nishi...
We propose a novel, motion planning based approach to approximately map the energy landscape of an RNA molecule. Our method is based on the successful probabilistic roadmap motion...
Xinyu Tang, Bonnie Kirkpatrick, Shawna L. Thomas, ...
This paper is concerned with on-line problems where a mobile robot of size D has to achieve a task in an unknown planar environment whose geometry is acquired by the robot during ...