RT Journal Article
T1 Gaussian process regression for forward and inverse kinematics of a soft robotic arm
A1 Relaño Gibert, Carlos
A1 Muñoz Mendi, Javier
A1 Monje Micharet, Concepción Alicia
AB The use of soft robotics to perform tasks and interact with the environment requires good system identification. Data-driven methods offer a promising alternative where traditional analytical model-based techniques have proven insufficient. However, their use has been limited and under-explored in soft robotics. The novelty of this research lies in the application of Gaussian processes to soft robotics and the exploration of approximate Gaussian processes (AGP) and deep Gaussian processes (DGP) methods. It highlights the advantages of Gaussian processes in modeling uncertainty, incorporating prior knowledge, and handling complex systems. This is achieved through the identification of the forward and inverse kinematics of a two-degree-of-freedom soft robotic arm actuated by three tendons. A comparison is made between different configurations using Gaussian processes and the results are also compared with those obtained from the analytical model of the kinematics and an artificial neural network (ANN). The research contributes to the development of more efficient and accurate techniques for system identification, kinematics modeling, and control in soft robotics.
PB Elsevier Ltd.
SN 0952-1976
YR 2023
FD 2023-11-01
LK https://hdl.handle.net/10016/39121
UL https://hdl.handle.net/10016/39121
LA eng
NO Acknowledgments. This research has been supported by the project SOFIA, with reference PID2020-113194GB-I00, funded by the Spanish Ministry of Economics, Industry and Competitiveness (MCIN/ AEI /10.13039/501100011033), and the project Desarrollo de articulaciones blandas para aplicaciones robóticas, with reference IND2020/IND-1739, funded by the Community of Madrid (CAM) (Department of Education and Research). All authors approved the version of the manuscript to be published.
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RD 17 jul. 2024