Task-Based Human-Robot Collaboration Control of Supernumerary Robotic Limbs for Overhead Tasks

Supernumerary robotic limbs (SRLs) are novel wearable robots that can be used to augment human operating ability in completing some difficult and complex tasks. In this work, a task-based human-SRLs collaboration control method for overhead tasks is developed. It is autonomous and safe without the need for active commands and previous data. A task model is proposed to model the human-SRLs collaboration process that features different task states and state transition conditions. Specifically, the overhead task process is modeled as a finite state machine (FSM) with four task states, three trigger events, and three SRLs actions. The real-time measured human motion data is utilized to trigger the task state transition and estimate the task parameters, which are the constraints for SRLs motion planning. The proposed admittance control with adjustable parameters allows SRLs to behave like spring-damping systems with different characteristics in different states and actions. The admittance control enhances the safety and reliability of the human-SRLs interaction. Finally, the effectiveness of the proposed control method for overhead tasks is further validated on a prototype of the human-SRLs system with two subjects under different installation heights. Trigger events and task parameters are successfully detected and estimated during the task process to trigger the coordination actions of SRLs. The results demonstrate that the task-based collaboration method is useful for overhead tasks with different task parameters.