scholarly journals Probabilistic Force Estimation and Event Localization (PFEEL) algorithm

2021 ◽  
pp. 113535
Author(s):  
Yohanna MejiaCruz ◽  
Zhaoshuo Jiang ◽  
Juan M. Caicedo ◽  
Jean M. Franco
Keyword(s):  
Force Estimation ◽  
Event Localization

Force Estimation and Event Localization (FEEL) of Impacts Using Structural Vibrations

2021 ◽  
Vol 147 (3) ◽  
pp. 04020154
Author(s):  
Benjamin T. Davis ◽  
Juan M. Caicedo ◽  
Victor A. Hirth
Keyword(s):  
Force Estimation ◽  
Structural Vibrations ◽  
Event Localization

Voltage- and Current-Measurement Based Force Estimation in Broaching Using Synchronous Motor Drive

2020 ◽  
Author(s):  
Kazumasa Miura ◽  
Tobias Seelbach ◽  
Thorsten Augspurger ◽  
Daniel Schraknepper ◽  
Thomas Bergs
Keyword(s):  
Synchronous Motor ◽  
Current Measurement ◽  
Motor Drive ◽  
Force Estimation ◽  
Synchronous Motor Drive

Sound Event Localization and Detection Based on Multiple DOA Beamforming and Multi-Task Learning

2020 ◽  
Author(s):  
Wei Xue ◽  
Ying Tong ◽  
Chao Zhang ◽  
Guohong Ding ◽  
Xiaodong He ◽  
...  
Keyword(s):  
Task Learning ◽  
Sound Event ◽  
Event Localization

scholarly journals Force estimation from 4D OCT data in a human tumor xenograft mouse model

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Maximilian Neidhardt ◽  
Nils Gessert ◽  
Tobias Gosau ◽  
Julia Kemmling ◽  
Susanne Feldhaus ◽  
...  
Keyword(s):  
Deep Learning ◽  
Mouse Model ◽  
Haptic Feedback ◽  
Human Tumor ◽  
Tumor Xenograft ◽  
Human Tumor Xenograft ◽  
Force Estimation ◽  
Xenograft Mouse Model ◽  
Dead Tissue

AbstractMinimally invasive robotic surgery offer benefits such as reduced physical trauma, faster recovery and lesser pain for the patient. For these procedures, visual and haptic feedback to the surgeon is crucial when operating surgical tools without line-of-sight with a robot. External force sensors are biased by friction at the tool shaft and thereby cannot estimate forces between tool tip and tissue. As an alternative, vision-based force estimation was proposed. Here, interaction forces are directly learned from deformation observed by an external imaging system. Recently, an approach based on optical coherence tomography and deep learning has shown promising results. However, most experiments are performed on ex-vivo tissue. In this work, we demonstrate that models trained on dead tissue do not perform well in in vivo data. We performed multiple experiments on a human tumor xenograft mouse model, both on in vivo, perfused tissue and dead tissue. We compared two deep learning models in different training scenarios. Training on perfused, in vivo data improved model performance by 24% for in vivo force estimation.

scholarly journals Sensorless environment stiffness and interaction force estimation for impedance control tuning in robotized interaction tasks

2021 ◽  
Author(s):  
Loris Roveda ◽  
Dario Piga
Keyword(s):  
Impedance Control ◽  
Interaction Force ◽  
Industrial Robots ◽  
Force Estimation ◽  
Interaction Control ◽  
Stiffness Properties ◽  
Implementation Effort ◽  
Coupling Dynamics ◽  
And Control ◽  
Assembly Tasks

AbstractIndustrial robots are increasingly used to perform tasks requiring an interaction with the surrounding environment (e.g., assembly tasks). Such environments are usually (partially) unknown to the robot, requiring the implemented controllers to suitably react to the established interaction. Standard controllers require force/torque measurements to close the loop. However, most of the industrial manipulators do not have embedded force/torque sensor(s) and such integration results in additional costs and implementation effort. To extend the use of compliant controllers to sensorless interaction control, a model-based methodology is presented in this paper. Relying on sensorless Cartesian impedance control, two Extended Kalman Filters (EKF) are proposed: an EKF for interaction force estimation and an EKF for environment stiffness estimation. Exploiting such estimations, a control architecture is proposed to implement a sensorless force loop (exploiting the provided estimated force) with adaptive Cartesian impedance control and coupling dynamics compensation (exploiting the provided estimated environment stiffness). The described approach has been validated in both simulations and experiments. A Franka EMIKA panda robot has been used. A probing task involving different materials (i.e., with different - unknown - stiffness properties) has been considered to show the capabilities of the developed EKFs (able to converge with limited errors) and control tuning (preserving stability). Additionally, a polishing-like task and an assembly task have been implemented to show the achieved performance of the proposed methodology.

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