Adaptive Gravity Compensation Framework Based on Human Upper Limb Model for Assistive Robotic Arm Extender

The Assistive Robotic Arm Extender (ARAE) is an upper limb assistive and rehabilitation robot that belongs to the end-effector type, enabling it to assist patients with upper limb movement disorders in three-dimensional space. However, the problem of gravity compensation for the human upper limb with this type of robot is crucial, which directly affects the deployment of the robot in the assistive or rehabilitation field. This paper presents an adaptive gravity compensation framework that calculates the compensated force based on the estimated human posture in 3D space. First, we estimated the human arm joint angles in real-time without any wearable sensors, such as inertial measurement unit (IMU) or magnetic sensors, only through the kinematic data of the robot and established human model. The performance of the estimation method was evaluated through a motion capture system, which validated the accuracy of joint angle estimation. Second, the estimated human joint angles were input to the rigid link model to demonstrate the support force profile generated by the robot. The force profile showed that the support force provided by the developed ARAE robot could adaptively change with human arm postures in 3D space. The adaptive gravity compensation framework can improve the usability and feasibility of the 3D end-effector rehabilitation or assistive robot.