The mobile robot EDAN (EMG-controlled daily assistant) is an assistive robotics system for people with severe motor impairments. The robot is controlled using muscle signals. Its integrated shared-autonomy capabilities facilitate everyday tasks such as drinking from a glass.
In 2016, EDAN was presented to the public for the first time.
EDAN is a robotic research platform for people with severe motor impairments. The sensitive lightweight robot arm and the five-finger hand ensure safety for the user and allow a wide range of interactions with the environment. Instead of the usual joystick, muscle signals are measured on the skin surface (EMG) and subsequently processed to generate motion commands for the robot. Even in cases of advanced muscular atrophy, individual muscle signals are often measurable, making the use of EDAN possible. To make use of the robot as easy as possible, so-called shared-control techniques are implemented. The robot uses its knowledge of the world to predict the intentions of the user and to assist accordingly in the execution of the task. If, for example, the robot detects that the intention is to grasp a glass for drinking, the motion commands decoded from the EMG-signals are adapted to guide the hand securely to the glass and to grasp it.
A video illustrating EDAN is available on YouTube.
Both the wheelchair and the robot arm mounted on it can be controlled by the person using EMG signals.
DLR (CC-BY 3.0).
The EDAN assistive robotics system enables physically disabled people to regain their day-to-day mobility.
The assistive robot EDAN is used to grasp a drinking bottle via EMG-control. Using this technology, people with severe physical impairments can regain a high degree of independence in their everyday lives.
People with physical disabilities, especially those affecting the upper limbs, are dependent on the help of others even for very simple daily activities. These people can be helped by robotic assistive systems in conjunction with a brain-machine interface to carry out such simple tasks autonomously and thereby increase their independence. For this purpose, we are investigating various methods to enable people with physical disabilities to control robotic arms.