Overview

brachI/Oplexus -- a digital nerve center for connecting human interfaces to robotic arms

Development of our earliest robotic arm mapping software began in 2009 and was previously known under the umbrella term “Myoelectric Training Tool”.  Since then the basic underlying structure of the software has been ported to several different languages and operating systems. Through years of direct feedback from clinical and research users we have expanded the features and improved the capabilities of our softwares.

The latest iteration of our software is called brachI/Oplexus and was developed in Visual Studio Express 2015 in C#. The software includes support for controlling our open source robotic platform – The Bento Arm – with an Xbox 360 controller, MYO armband via muscle signals, or keyboard and is  available open source on github. Future releases will include additional human interfaces and robots such as the HANDi Hand.

The following runthrough video gives an overview of some of the core features and how to use the software to get the robots moving:

The rationale for developing brachI/Oplexus was to provide a simple windows desktop application for users that want to control our robots out of the box without necessarily having to jump into coding right away. To facilitate this, brachI/Oplexus includes an installer package that simplifies installation on Windows based computers. However, we also provide the source files and fully documented code for advanced users who want to modify the software for their application or research.

NOTE1: brachI/Oplexus is pronounced ‘brack-I-O-plexus’ and is inspired by the anatomical term ‘brachial plexus’ which is the main network of nerves that connects the brain and spinal cord to your arm.

NOTE2: In addition brachI/Oplexus we also have two alternate custom softwares — one developed in the Robot Operating System (ROS) which is an open source framework for developing robotic systems and another developed in Matlab’s Simulink Realtime. Both softwares include mapping functionality to allow for control interfaces such as joysticks or muscle signals to be easily mapped to the joint movements on the arm, but have not yet been released open source. Please contact us if you would to have early access to these alternate softwares.

Acknowledgements:

We would like to thank the Alberta Machine Intelligence Institute (Amii) and the University of Alberta for their continued support in this project.