Enhancing Human–Robot Collaboration: Supernumerary Robotic Limbs for Object Balance

Abstract

Supernumerary robotic limb (SRL) is recognized as being at the forefront of robotics innovation, aimed at augmenting human capabilities in complex working environments. Despite their potential to significantly enhance operational efficiency, the integration of SRL for dynamic and intricate tasks presents challenges in teleoperation, precise positioning, and dynamic balance control. To address challenges in initiating control when targets or the SRL’s end-effector are outside the camera’s visual range, a coarse teleoperation strategy is implemented. This strategy utilizes the inertial measurement unit (IMU) and the extended Kalman filter (EKF), enabling basic orientation and movement toward the target area without reliance on visual cues. Challenges in achieving fine-tuned control for accurate task completion, particularly in visual navigation and precise positioning of the SRL’s end-effector, are addressed by integrating object detection via YOLOX with the tangential artificial potential field (T-APF) method for exact path planning. This integration significantly enhances the system’s ability to fine-tune the placement of end-effector. The challenge of conducting balance tasks without force sensors is tackled by adopting a dual-spring model combined with autoregressive (AR) predictive modeling, enabling effective balance support through anticipatory motion adjustments. Experiments have demonstrated the system’s enhanced positional accuracy and maintained synchronization with human movements, underscoring the effectiveness of the integrated approach in facilitating complex human-robot collaborative tasks.