Abstract
In this paper, we propose a method for the estimation of the interaction forces between the motorized system and object through the visual and electric information. In particular, we propose a new interaction force sensing method based on sequential images and the electrical current from the motor during the interaction between the system and environment to estimate the interaction force using deep learning. In the previous method, to measure the interaction force using only visual information, the prediction is inaccurate when the system interacts with an undeformable target, even though the aspect of the change appears small in the image. We use a neural network structure for estimating the interaction force from the time-series data of visual and electric information using deep learning, which combines the convolution neural network and long short-term memory models. From the evaluation to show the feasibility of the interaction force estimation, the proposed learning models successfully estimate the forces for four targets (rigid box, rigid box on sponge, sponge, and stapler), which are both deformable and undeformable objects. The proposed method demonstrates the best results in the interaction force estimation between the motorized system and object.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.