Distributed Control for Collaborative Robotic Systems Using 5G Edge Computing

Autor: Dominik Urbaniak, Sebastian Bro Damsgaard, Weifan Zhang, Jan Rosell, Raul Suarez, Michael Suppa

Publikováno v: IEEE Access, Vol 12, Pp 148706-148718 (2024)

Collaborative and mobile robotics for industrial environments promise to enable autonomous and flexible production processes. However, this vision also poses significant challenges to the robotic systems, requiring them to adapt to dynamic environmen

Externí odkaz: https://doaj.org/article/be9296d4f05043e1adf6a0af0c72f3d7

Learning-Based Planner for Unknown Object Dexterous Manipulation Using ANFIS

Autor: Mohammad Sheikhsamad, Raúl Suárez, Jan Rosell

Publikováno v: Machines, Vol 12, Iss 6, p 364 (2024)

Dexterous manipulation of unknown objects performed by robots equipped with mechanical hands represents a critical challenge. The difficulties arise from the absence of a precise model of the manipulated objects, unpredictable environments, and limit

Externí odkaz: https://doaj.org/article/98f568e501a54e2598222ba632943c1c

Contingent Task and Motion Planning under Uncertainty for Human–Robot Interactions

Autor: Aliakbar Akbari, Mohammed Diab, Jan Rosell

Publikováno v: Applied Sciences, Vol 10, Iss 5, p 1665 (2020)

Manipulation planning under incomplete information is a highly challenging task for mobile manipulators. Uncertainty can be resolved by robot perception modules or using human knowledge in the execution process. Human operators can also collaborate w

Externí odkaz: https://doaj.org/article/0b36b52fb9884776b7f7c9342c1a3149

Efficient Industrial Solution for Robotic Task Sequencing Problem With Mutual Collision Avoidance & Cycle Time Optimization

Autor: Hicham Touzani, Nicolas Seguy, Hicham Hadj-Abdelkader, Raul Suarez, Jan Rosell, Leopold Palomo-Avellaneda, Samia Bouchafa

Publikováno v: IEEE Robotics and Automation Letters
IEEE Robotics and Automation Letters, 2022, 7 (2), pp.2597--2604. ⟨10.1109/LRA.2022.3142919⟩
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Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1a1562d51927b4a27c4865d901034653
https://hdl.handle.net/2117/366461

A Dataset Generation Tool for Deep learning-based Motion Planning in Complex Environments

Autor: Muhammad Usman Sarwar, Wajahat M. Qazi, Jan Rosell, Muhayy Ud Din, Moman Sohail

Publikováno v: 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ).

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::1110282f7b0923e5c71df4b29e127be5
https://doi.org/10.1109/etfa45728.2021.9613554