Zobrazeno 1 - 10
of 92
pro vyhledávání: '"Angela P Schoellig"'
Publikováno v:
IEEE Robotics and Automation Letters. 7:10152-10159
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5e4a9910d2ef61085b9c537c62f14ca5
https://doi.org/10.1109/lra.2022.3193242
https://doi.org/10.1109/lra.2022.3193242
Exploiting Differential Flatness for Robust Learning-Based Tracking Control Using Gaussian Processes
Autor:
Melissa Greeff, Angela P. Schoellig
Publikováno v:
IEEE Control Systems Letters. 5:1121-1126
Learning-based control has shown to outperform conventional model-based techniques in the presence of model uncertainties and systematic disturbances. However, most state-of-the-art learning-based nonlinear trajectory tracking controllers still lack
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b6e737df62c2ef8773a920de94c38cf3
https://doi.org/10.1109/lcsys.2020.3009177
https://doi.org/10.1109/lcsys.2020.3009177
Publikováno v:
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6b682368d8826eace7db55846b1f014c
https://doi.org/10.1109/iros47612.2022.9981520
https://doi.org/10.1109/iros47612.2022.9981520
Publikováno v:
International Journal of Robust and Nonlinear Control. 31:8750-8784
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::655157b7e469ff800af3726aa23b3215
https://doi.org/10.1002/rnc.5712
https://doi.org/10.1002/rnc.5712
Publikováno v:
IEEE Control Systems Letters. 5:923-928
In the robotics literature, experience transfer has been proposed in different learning-based control frameworks to minimize the costs and risks associated with training robots. While various works have shown the feasibility of transferring prior exp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2b0905434c97f680fb3b4e8d0f20ceba
https://doi.org/10.1109/lcsys.2020.3005886
https://doi.org/10.1109/lcsys.2020.3005886
Publikováno v:
IEEE Robotics and Automation Letters. 6:3240-3247
This paper presents a model-learning method for Stochastic Model Predictive Control (SMPC) that is both accurate and computationally efficient. We assume that the control input affects the robot dynamics through an unknown (but invertable) nonlinear
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1c88278ea02af294ed95383bf6754e65
https://doi.org/10.1109/lra.2021.3063957
https://doi.org/10.1109/lra.2021.3063957
Autor:
Ali Mesbah, Kim P. Wabersich, Angela P. Schoellig, Melanie N. Zeilinger, Sergio Lucia, Thomas A. Badgwell, Joel A. Paulson
Publikováno v:
2022 American Control Conference (ACC).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fdaa9e9bac1f520ab5d74b6b08d8ab02
https://doi.org/10.23919/acc53348.2022.9867643
https://doi.org/10.23919/acc53348.2022.9867643
Ultra-wideband (UWB) time difference of arrival (TDOA)-based localization has recently emerged as a promising indoor positioning solution. However, in cluttered environments, both the UWB radio positions and the obstacle-induced non-line-of-sight (NL
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5644a69eb2aad44ff8e3988d1b7f1085
http://arxiv.org/abs/2204.04508
http://arxiv.org/abs/2204.04508
Publikováno v:
The International Journal of Robotics Research. 39:1397-1418
High-accuracy trajectory tracking is critical to many robotic applications, including search and rescue, advanced manufacturing, and industrial inspection, to name a few. Yet the unmodeled dynamics and parametric uncertainties of operating in such co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::749a492783d5d4200e5db23bc2a72f93
https://doi.org/10.1177/0278364920953902
https://doi.org/10.1177/0278364920953902
Autor:
Kaicheng Zhang, Keenan Burnett, Linqiao Liu, Michael J. Sorocky, Sepehr Samavi, Timothy D. Barfoot, Arkady Arkhangorodsky, Angela P. Schoellig, Mollie Bianchi, Yizhou Huang, Jingxing Qian, Quinlan Sykora, Susan Sun, Xintong Du, Tianchang Shen, Shichen Lu, David J. Yoon
Publikováno v:
Journal of Field Robotics. 38:139-166
The SAE AutoDrive Challenge is a three-year collegiate competition to develop a self-driving car by 2020. The second year of the competition was held in June 2019 at MCity, a mock town built for self-driving car testing at the University of Michigan.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::78f932d7d88aa0fa6c8b03ac57c2f8a7
https://doi.org/10.1002/rob.21958
https://doi.org/10.1002/rob.21958