TY - JOUR
T1 - Learning configurations of wires for real-time shape estimation and manipulation planning
AU - Mishani, Itamar
AU - Sintov, Avishai
N1 - Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/5
Y1 - 2023/5
N2 - Robotic manipulation of a wire by its ends requires rapid reasoning of its shape in real-time. A recent development of an analytical model has shown that sensing of the force and torque on one end can be used to determine its shape. However, the model relies on assumptions that may not be met in real world wires and do not take into account gravity and non-linearity of the Force/Torque (F/T) sensor. Hence, the model cannot be applied to any wire with accurate shape estimation. In this paper, we explore the learning of a model to estimate the shape of a wire based solely on measurements of F/T states and without any visual perception. Visual perception is only used for off-line data collection. We propose to train a Supervised Autoencoder with convolutional layers that reconstructs the spatial shape of the wire while enforcing the latent space to resemble the space of F/T. Then, the encoder operates as a descriptor of the wire where F/T states can be mapped to its shape. On the other hand, the decoder of the model is the inverse problem where a desired goal shape can be mapped to the required F/T state. With the same collected data, we also learn the mapping from F/T states to grippers poses. Then, a motion planner can plan a path within the F/T space to a goal while avoiding obstacles. We validate the proposed data-based approach on Nitinol and standard electrical wires, and demonstrate the ability to accurately estimate their shapes.
AB - Robotic manipulation of a wire by its ends requires rapid reasoning of its shape in real-time. A recent development of an analytical model has shown that sensing of the force and torque on one end can be used to determine its shape. However, the model relies on assumptions that may not be met in real world wires and do not take into account gravity and non-linearity of the Force/Torque (F/T) sensor. Hence, the model cannot be applied to any wire with accurate shape estimation. In this paper, we explore the learning of a model to estimate the shape of a wire based solely on measurements of F/T states and without any visual perception. Visual perception is only used for off-line data collection. We propose to train a Supervised Autoencoder with convolutional layers that reconstructs the spatial shape of the wire while enforcing the latent space to resemble the space of F/T. Then, the encoder operates as a descriptor of the wire where F/T states can be mapped to its shape. On the other hand, the decoder of the model is the inverse problem where a desired goal shape can be mapped to the required F/T state. With the same collected data, we also learn the mapping from F/T states to grippers poses. Then, a motion planner can plan a path within the F/T space to a goal while avoiding obstacles. We validate the proposed data-based approach on Nitinol and standard electrical wires, and demonstrate the ability to accurately estimate their shapes.
KW - Convolutional autoencoder
KW - Elastic wires
KW - Shape estimation
UR - http://www.scopus.com/inward/record.url?scp=85147854411&partnerID=8YFLogxK
U2 - 10.1016/j.engappai.2023.105967
DO - 10.1016/j.engappai.2023.105967
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AN - SCOPUS:85147854411
SN - 0952-1976
VL - 121
JO - Engineering Applications of Artificial Intelligence
JF - Engineering Applications of Artificial Intelligence
M1 - 105967
ER -