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.
Highlights
In the fields of robotics and biomedical engineering, there is a growing trend in designing assistive robots to aid users in demanding tasks. e design parameters for these robots involve a detailed understanding of the operating environment, intended task, and user-control inputs
Is paper focuses on the implementation of an assistive hybrid brain-computer interface (BCI) semiautonomous mobile robotic arm for users with debilitating paralysis or progressive nervous system diseases
Forward simulations which result in collisions are immediately discredited. e trajectory of the best forward simulation is sent to the robot system and the Dynamic Window Approach (DWA) planner performs another round of simulations based on the change of location of the robot since the last simulation. e deployment of this Simultaneous Localization and Mapping (SLAM) algorithm permits the robot to safely navigate from one goal to the next. e routine checks accurately account for the robot and allow for precise remote BCI control. ey keep the robot from crashing into any obstacles which may have been moved and account for any delays in the data stream
Summary
In the fields of robotics and biomedical engineering, there is a growing trend in designing assistive robots to aid users in demanding tasks. e design parameters for these robots involve a detailed understanding of the operating environment, intended task, and user-control inputs. A set of specific physical or mental tasks (i.e., jaw clench and imagined left/right hand squeeze) are mapped to robot commands with some user training Receiving these commands, a mobile robotic arm is a Journal of Robotics solution for everyday object manipulation and transportation in household and occupation environments. While health policymakers and social organizations can certainly improve aid to the disabled, the deployment of semiautonomous mobile robotic assistive systems can empower one of the more severe handicap conditions, a quadriplegic, to perform daily tasks at home that normally require personnel in stay-at-home scenarios. E assistive robotic system presented in this paper is designed to operate solely on the user’s brain signals Since this proposed mobile robotic arm is separate from a wheelchair, the end user can retrieve objects without having the hassle of leaving his/her current position
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