20.500.12556/RUL-136699 End-effector force and joint torque estimation of a 7-DoF robotic manipulator using deep learning When a mobile robotic manipulator interacts with other robots, people, or the environment in general, the end-effector forces need to be measured to assess if a task has been completed successfully. Traditionally used force or torque estimation methods are usually based on observers, which require knowledge of the robot dynamics. Contrary to this, our approach involves two methods based on deep neural networks: robot end-effector force estimation and joint torque estimation. These methods require no knowledge of robot dynamics and are computationally effective but require a force sensor under the robot base. Several different architectures were considered for the tasks, and the best ones were identified among those tested. First, the data for training the networks were obtained in simulation. The trained networks showed reasonably good performance, especially using the LSTM architecture (with a root mean squared error (RMSE) of 0.1533 N for end-effector force estimation and 0.5115 Nm for joint torque estimation). Afterward, data were collected on a real Franka Emika Panda robot and then used to train the same networks for joint torque estimation. The obtained results are slightly worse than in simulation (0.5115 Nm vs. 0.6189 Nm, according to the RMSE metric) but still reasonably good, showing the validity of the proposed approach. robotic manipulator force estimation deep learning neural networks robotski manipulator ocena sile interakcije globoko učenje nevronske mreže true false true Angleški jezik Slovenski jezik Članek v reviji 2022-05-16 15:11:38 2022-05-16 15:11:39 2022-09-15 04:14:12 0000-00-00 00:00:00 2021 0 0 18 str. iss. 23, art. 2963 Vol. 10 Dec. 2021 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 007.52 2079-9292 10.3390/electronics10232963 87856643 electronics-10-02963-v2.pdf electronics-10-02963-v2.pdf 1 0C7E36732B2514C9C75717A2C5C9F21D 1131145c4b58a4a625026e49b0a6ae4e80fb1f3a2821daf14b170c7b8d3eca37 d5eb7bfa-d519-11ec-8aca-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=156447 https://www.mdpi.com/2079-9292/10/23/2963 1 9ba73e12-d519-11ec-8aca-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=156446 Fakulteta za elektrotehniko 0 0 0