Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Schroepfer, Pete"'
Autor:
Batista, Luis F W, Ro, Junghwan, Richard, Antoine, Schroepfer, Pete, Hutchinson, Seth, Pradalier, Cedric
Despite the increasing adoption of Deep Reinforcement Learning (DRL) for Autonomous Surface Vehicles (ASVs), there still remain challenges limiting real-world deployment. In this paper, we first integrate buoyancy and hydrodynamics models into a mode
Externí odkaz:
http://arxiv.org/abs/2407.08263
Autor:
Schroepfer, Pete, Schauffel, Nathalie, Gründling, Jan, Ellwart, Thomas, Weyers, Benjamin, Pradalier, Cédric
This paper outlines a roadmap to effectively leverage shared mental models in multi-robot, multi-stakeholder scenarios, drawing on experiences from the BugWright2 project. The discussion centers on an autonomous multi-robot systems designed for ship
Externí odkaz:
http://arxiv.org/abs/2312.08047
Autor:
Schroepfer, Pete, Gründling, Jan P., Schauffel, Nathalie, Oehrl, Simon, Pape, Sebastian, Kuhlen, Torsten W., Weyers, Benjamin, Ellwart, Thomas, Pradalier, Cédric
Publikováno v:
ACM/IEEE International Conference on Human-Robot Interaction; Mar2024, p630-638, 9p
Autor:
Chahine, Georges1,2 (AUTHOR) gchahine@gatech.edu, Schroepfer, Pete1,2 (AUTHOR) cedricp@georgiatech-metz.fr, Ouabi, Othmane-Latif2 (AUTHOR), Pradalier, Cédric2 (AUTHOR)
Publikováno v:
Sensors (14248220). May2022, Vol. 22 Issue 9, p3235-3235. 14p.
This article considers the challenge of recovering an initial topology of a mesh of devices based on noisy and incomplete measurements of their inter-distances. In comparison to earlier approaches, this paper provides a first guess of the topology, a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3379::669cc907d64df8c88ba7e6ae2a27d8be
https://hal.science/hal-03965253/file/IROS22_UWB_SelfCalibration.pdf
https://hal.science/hal-03965253/file/IROS22_UWB_SelfCalibration.pdf