TY - GEN
T1 - Dynamic Modeling of a Soft Laparoscope
T2 - 19th Latin American Robotics Symposium, 14th Brazilian Symposium on Robotics and 13th Workshop on Robotics in Education, LARS-SBR-WRE 2022
AU - Cespedes, Axel
AU - Terreros, Ricardo
AU - Morales, Sergio
AU - Huamani, Aldair
AU - Canahuire, Ruth
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Soft robotics is a research area with a diverse number of designs and shapes depending on the application. It is due to this variety that the modeling of soft robots is reduced to a few methods. However, a model based on neural networks simplifies and connects the necessary variables to perform tasks in real-time. This model relates the coordinates of the end effector in the Cartesian plane with the inputs of the soft actuator, which are the internal pressures of each chamber. In addition, a model based on neural networks considers the limitations of the system since the base data for learning is similar to real conditions data. With this approach and the piecewise constant curvature kinematic modeling, the position and orientation in the workspace of the soft laparoscope can be accurately identified.
AB - Soft robotics is a research area with a diverse number of designs and shapes depending on the application. It is due to this variety that the modeling of soft robots is reduced to a few methods. However, a model based on neural networks simplifies and connects the necessary variables to perform tasks in real-time. This model relates the coordinates of the end effector in the Cartesian plane with the inputs of the soft actuator, which are the internal pressures of each chamber. In addition, a model based on neural networks considers the limitations of the system since the base data for learning is similar to real conditions data. With this approach and the piecewise constant curvature kinematic modeling, the position and orientation in the workspace of the soft laparoscope can be accurately identified.
KW - Soft robotics
KW - end effector and soft laparoscope
KW - modeling
KW - neural networks
UR - http://www.scopus.com/inward/record.url?scp=85146725680&partnerID=8YFLogxK
U2 - 10.1109/LARS/SBR/WRE56824.2022.9995831
DO - 10.1109/LARS/SBR/WRE56824.2022.9995831
M3 - Conference contribution
AN - SCOPUS:85146725680
T3 - 2022 19th Latin American Robotics Symposium, 2022 14th Brazilian Symposium on Robotics and 2022 13th Workshop on Robotics in Education, LARS-SBR-WRE 2022
SP - 253
EP - 258
BT - 2022 19th Latin American Robotics Symposium, 2022 14th Brazilian Symposium on Robotics and 2022 13th Workshop on Robotics in Education, LARS-SBR-WRE 2022
A2 - Homem, Thiago Pedro Donadon
A2 - da Costa Bianchi, Reinaldo Augusto
A2 - da Silva, Bruno Marques Ferreira
A2 - da Costa Fernandes Curvelo, Carla
A2 - Pinto, Milena Faria
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 18 October 2022 through 21 October 2022
ER -