Sensors, Vol. 19, Pages 3636: Towards Model-Free Tool Dynamic Identification and Calibration Using Multi-Layer Neural Network

Sensors, Vol. 19, Pages 3636: Towards Model-Free Tool Dynamic Identification and Calibration Using Multi-Layer Neural Network Sensors doi: 10.3390/s19173636 Authors: Hang Su Wen Qi Yingbai Hu Juan Sandoval Longbin Zhang Yunus Schmirander Guang Chen Andrea Aliverti Alois Knoll Giancarlo Ferrigno Elena De Momi In robot control with physical interaction, like robot-assisted surgery and bilateral teleoperation, the availability of reliable interaction force information has proved to be capable of increasing the control precision and of dealing with the surrounding complex environments. Usually, force sensors are mounted between the end effector of the robot manipulator and the tool for measuring the interaction forces on the tooltip. In this case, the force acquired from the force sensor includes not only the interaction force but also the gravity force of the tool. Hence the tool dynamic identification is required for accurate dynamic simulation and model-based control. Although model-based techniques have already been widely used in traditional robotic arms control, their accuracy is limited due to the lack of specific dynamic models. This work proposes a model-free technique for dynamic identification using multi-layer neural networks (MNN). It utilizes two types of MNN architectures based on both feed-forward networks (FF-MNN) and cascade-forward networks (CF-MNN) to model the tool dynamics. Compared with the model-based technique, i.e., curve fitt...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research