CPS presents a guide to setup the communication between Micro – ROS and ROS2 and control multiple servos attached to a PWM/Servo Driver board. Therefor the installation of ROS2, the setup of a Micro – ROS workspace, the establishment of the communication with a custom message and the implementation of third party libraries as well as the usage of two different RTOS system will be described step for step.

By combining the power of Micro ROS Foxy, ESP32 and a PCA9685 board, this project provides a way of controlling multiple servos. The setup has been tested on a Linux Ubuntu 20.04.6 LTS environment, allowing seamless communication and accurate servo positioning. The linked guide give some information about Micro – ROS and walks through the installation of ROS2, the setup of a Micro-ROS workspace, and the establishment of the communication between Micro-ROS and ROS2 using a custom message format called “ServoMessage”. Additionally, the guide covers the implementation of third-party libraries and the usage of two different RTOS systems. The code includes examples of how to use the Micro ROS Foxy framework to send and receive ServoMessages over the ROS2 network to control the attached servos. 

Components

Electronics:

• Micro USB Cable
• ESP32 Developement Board
• 2 x 4,7 kΩ resistors 
• PCA9685 PWM/Servo Board
• Servos
• 5V Power Supply

Links

• Step for Step Guide: https://cloud.cps.unileoben.ac.at/index.php/s/G888TecHBFELWiw
• Repository: https://github.com/MaxPett/Micro-ROS_Servo

We are developing a repository for real-time control of the FRANKA EMIKA Panda 7-dof robot arm.

Our project is based on ROS and allows to teleoperate the robot arm in real-time using motion tracking data provided by OptiTrack’s Motive software. 

GitHub Project and Links

• https://github.com/ai-lab-science/Franka-ROS-TRAIN
• Details on the robot hardware
• Or on the developer site on world.franka.de
• ROS Gazebo Tutorial

Videos

• Research videos using the robot will be presented here. 

Publications

2020

Xue, H.; Boettger, S.; Rottmann, N.; Pandya, H.; Bruder, R.; Neumann, G.; Schweikard, A.; Rueckert, E. Sample-Efficient Covariance Matrix Adaptation Evolutional Strategy via Simulated Rollouts in Neural Networks Proceedings Article In: International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI’ 2020), 2020. Links | BibTeX @inproceedings{Xue2020, title = {Sample-Efficient Covariance Matrix Adaptation Evolutional Strategy via Simulated Rollouts in Neural Networks}, author = {H. Xue and S. Boettger and N. Rottmann and H. Pandya and R. Bruder and G. Neumann and A. Schweikard and E. Rueckert}, url = {https://cps.unileoben.ac.at/wp/ASPAI2020Xue.pdf, Article File}, year = {2020}, date = {2020-06-30}, booktitle = {International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI’ 2020)}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Close Article File Close

Nils Rottmann, M.Sc. has developed a tutorial on using ROS and Gazebo. 

This tutorial was used in our humanoid robotics and machine learning lectures. 

GitHub Code & Links

• https://github.com/NRottmann/ROS_Gazebo_Tutorial

Weitere Links und Tutorials

• Python Videolectures
• Python Online Tutorials

Dieses open-source Projekt enthält Tools und Demos für die Python-Entwicklung mit den Lego Mindstorms EV3 und EV3Dev Bricks. 

Die Inhalte sind verständlich aufbereitet und wir haben zahlreiche Tutorials und Aufgaben für Schüler*innen erstellt. 

GitHub Code & Links

• https://github.com/ai-lab-science/LEGORoboticsPython
• Dieses Repository ist Teil unseres Lego Robotik Schüler*innen Projekts.  

Details to the Software Development

Dieser Einführungsvortrag beschreibt die grundlegenden Schritte um einen LEGO Roboter zu bauen und mit Python zu programmieren. 

Weitere Links und Tutorials

• Wiki: https://github.com/rueckert/LEGORoboticsPython/wiki
• Installation und Dokumentation von Micropython.

Sensor gloves are gaining importance in tracking hand and finger movements in virtual reality applications as well as in scientific research. In this project, we developed  a low-budget, yet accurate sensor glove system that uses flex sensors for fast and efficient motion tracking. 

The contributions are ROS Interfaces, simulation models as well as motion modeling approaches. 

GitHub Code & Links

• https://github.com/ai-lab-science/SensorGloves
• The software development is part of the TRAIN project. 
• The development is based on prior sensor glove designs.

Details to the Software Development

The figure shows a simplified schematic diagram of the system architecture for our sensor glove design:

(a) Glove layout with sensor placements, the orange fields denote the flex sensors, while the IMU is marked as a green rectangle,

(b) Circuit board which is wired with the sensor glove, has 10 voltage dividers for reading each flex sensor connected to ADC pins of the microcontoller ESP32-S2 and the IMU is connected to I2C pins,

(c) The ESP32-S2 sends the raw data via WiFi as ROS messages to the computer, which allows a real-time visualization in Unity or Gazebo,

(d) Post-processing of the recorded data, e.g. learning probabilistic movement models and searching for similarities.

Publications

A research publication by Robin Denz, Rabia Demirci, M. Ege Cansev, Adna Bliek, Philipp Beckerle, Elmar Rueckert and Nils Rottmann is currently under review. 

Matlab Code Link

https://cps.unileoben.ac.at/wp/resources/code/MATLAB_ProbabilisticTrajectoryModel_2016Rueckert.zip

Publication where the Code was used

2016

Rueckert, Elmar; Camernik, Jernej; Peters, Jan; Babic, Jan Probabilistic Movement Models Show that Postural Control Precedes and Predicts Volitional Motor Control Journal Article In: Nature Publishing Group: Scientific Reports, vol. 6, no. 28455, 2016. Links | BibTeX @article{Rueckert2016b, title = {Probabilistic Movement Models Show that Postural Control Precedes and Predicts Volitional Motor Control}, author = {Elmar Rueckert and Jernej Camernik and Jan Peters and Jan Babic}, url = {https://cps.unileoben.ac.at/wp/SciReps_HumanContacts.pdf, Article File https://cps.unileoben.ac.at/wp/resources/code/MATLAB_ProbabilisticTrajectoryModel_2016Rueckert.zip, MATLAB Code https://cps.unileoben.ac.at/wp/SciReps_HumanContacts_Supplement.pdf, Supplement}, doi = {10.1038/srep28455}, year = {2016}, date = {2016-06-02}, journal = {Nature Publishing Group: Scientific Reports}, volume = {6}, number = {28455}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close Article File MATLAB Code Supplement doi:10.1038/srep28455 Close

Matlab Code Link

https://cps.unileoben.ac.at/wp/resources/code/MATLAB_SpikingNeuralPlanning_2016Rueckert.zip

Publication where the Code was used

2016

Rueckert, Elmar; Kappel, David; Tanneberg, Daniel; Pecevski, Dejan; Peters, Jan Recurrent Spiking Networks Solve Planning Tasks Journal Article In: Nature Publishing Group: Scientific Reports, vol. 6, no. 21142, 2016. Links | BibTeX @article{Rueckert2016a, title = {Recurrent Spiking Networks Solve Planning Tasks}, author = {Elmar Rueckert and David Kappel and Daniel Tanneberg and Dejan Pecevski and Jan Peters}, url = {https://cps.unileoben.ac.at/wp/SciReps_NeuralPlanning.pdf, Article File https://cps.unileoben.ac.at/wp/SciReps_NeuralPlanning_Supplement.pdf, Supplement https://cps.unileoben.ac.at/wp/resources/code/MATLAB_SpikingNeuralPlanning_2016Rueckert.zip, MATLAB Code}, doi = {10.1038/srep21142}, year = {2016}, date = {2016-01-15}, journal = {Nature Publishing Group: Scientific Reports}, volume = {6}, number = {21142}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close Article File Supplement MATLAB Code doi:10.1038/srep21142 Close