José Patiño
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
José Patiño has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
- Carlos Saldarriaga
Escuela Superior Politécnica del Litoral
1 shared publication - Christian Tutivén
Facultad de Ingeniería en Mecánica y Ciencias de la Producción, Escuela Superior Politécnica del Litoral, ESPOL, Campus Gustavo Galindo, Km. 30.5 Vía Perimetral, Guayaquil 090902
1 shared publication - Dennys Paillacho
Escuela Superior Politécnica del Litoral, ESPOL, Guayaquil, Ecuador
1 shared publication - Imin Kao
Stony Brook University
1 shared publication - José Luis Sampietro
Facultad de Ingenierías, Universidad ECOTEC, Km. 13.5 Samborondón, Samborondón EC092302, Ecuador
1 shared publication - Juan David Hernández
Cardiff University
1 shared publication
Latest Publications
- Open Access
- 30 November 2024
- Published by Association for Computing Machinery (ACM)
- p. 442-455
Exploring the Perceptions and Challenges of Social Robot Navigation: Two Case Studies in Different Socio-Technical Contexts
- 30 November 2024
- Published by Association for Computing Machinery (ACM)
- p. 442-455
Open AccessExploring the Perceptions and Challenges of Social Robot Navigation: Two Case Studies in Different Socio-Technical Contexts
- Open Access
- 17 February 2023
- Published by MDPI AG in Mathematics
- Vol. 11 (4) , 1021
Damping Ratio Prediction for Redundant Cartesian Impedance-Controlled Robots Using Machine Learning Techniques
Implementing impedance control in Cartesian task space or directly at the joint level is a popular option for achieving desired compliance behavior for robotic manipulators performing tasks. The damping ratio is an important control criterion for modulating the dynamic response; however, tuning or selecting this parameter is not easy, and can be even more complicated in cases where the system cannot be directly solved at the joint space level. Our study proposes a novel methodology for calculating the local optimal damping ratio value and supports it with results obtained from five different scenarios. We carried out 162 different experiments and obtained the values of the inertia, stiffness, and damping matrices for each experiment. Then, data preprocessing was carried out to select the most significant variables using different criteria, reducing the seventeen initial variables to only three. Finally, the damping ratio values were calculated (predicted) using automatic regression tools. In particular, five-fold cross-validation was used to obtain a more generalized model and to assess the forecasting performance. The results show a promising methodology capable of calculating and predicting control parameters for robotic manipulation tasks.
- 17 February 2023
- Published by MDPI AG in Mathematics
- Vol. 11 (4) , 1021
Open AccessDamping Ratio Prediction for Redundant Cartesian Impedance-Controlled Robots Using Machine Learning Techniques
Implementing impedance control in Cartesian task space or directly at the joint level is a popular option for achieving desired compliance behavior for robotic manipulators performing tasks. The damping ratio is an important control criterion for modulating the dynamic response; however, tuning or selecting this parameter is not easy, and can be even more complicated in cases where the system cannot be directly solved at the joint space level. Our study proposes a novel methodology for calculating the local optimal damping ratio value and supports it with results obtained from five different scenarios. We carried out 162 different experiments and obtained the values of the inertia, stiffness, and damping matrices for each experiment. Then, data preprocessing was carried out to select the most significant variables using different criteria, reducing the seventeen initial variables to only three. Finally, the damping ratio values were calculated (predicted) using automatic regression tools. In particular, five-fold cross-validation was used to obtain a more generalized model and to assess the forecasting performance. The results show a promising methodology capable of calculating and predicting control parameters for robotic manipulation tasks.