A Brain-Computer Interface for Teleoperation of a Semiautonomous Mobile Robotic Assistive System Using SLAM

Abstract

The proposed assistive hybrid brain-computer interface (BCI) semiautonomous mobile robotic arm demonstrates a design that is (1) adaptable by observing environmental changes with sensors and deploying alternate solutions and (2) versatile by receiving commands from the user’s brainwave signals through a noninvasive electroencephalogram cap. Composed of three integrated subsystems, a hybrid BCI controller, an omnidirectional mobile base, and a robotic arm, the proposed robot has commands mapped to the user’s brainwaves related to a set of specific physical or mental tasks. The implementation of sensors and the camera systems enable both the mobile base and the arm to be semiautonomous. The mobile base’s SLAM algorithm has obstacle avoidance capability and path planning to assist the robot maneuver safely. The robot arm calculates and deploys the necessary joint movement to pick up or drop off a desired object selected by the user via a brainwave controlled cursor on a camera feed. Validation, testing, and implementation of the subsystems were conducted using Gazebo. Communication between the BCI controller and the subsystems is tested independently. A loop of prerecorded brainwave data related to each specific task is used to ensure that the mobile base command is executed; the same prerecorded file is used to move the robot arm cursor and initiate a pick-up or drop-off action. A final system test is conducted where the BCI controller input moves the cursor and selects a goal point. Successful virtual demonstrations of the assistive robotic arm show the feasibility of restoring movement capability and autonomy for a disabled user.

A Novel Tongue-based Remote Control and the Implication of Adaptive Semi-automation, Fixed Semi-Automation and Manual Control of a Wheelchair mounted Assistive Robotic Manipulator

“…Current important studies on control interfaces for ARMs for severely disabled individuals have focused on different input modalities, such as head gestures [11], tongue control [12], [13], voice recognition [14], eye gaze [15], and brain computer interface [16]. When it comes to remote setting, BCI [17], [18], head array [19], sip/puff [19] and web browserbased interface [20] have been used as interfaces but for use in tetraplegia, often they are challenged by a lack of available command, not providing a continuous input signal, sensitivity to noise and more. Further, intuitive control has been researched as an important feature [21], [22].…”