A mixed perception-based human-robot collaborative maintenance approach driven by augmented reality and online deep reinforcement learning

Owing to the fact that the number and complexity of machines is increasing in Industry 4.0, the maintenance process is more time-consuming and labor-intensive, which contains plenty of refined maintenance operations. Fortunately, human-robot collaboration (HRC) can integrate human intelligence into the collaborative robot (cobot), which can realize not merely the nimble and sapiential maintenance operations of personnel but also the reliable and repeated maintenance manipulation of cobots. However, the existing HRC maintenance lacks the precise understand of the maintenance intention, the efficient HRC decision-making for executing robotized maintenance tasks (e.g., repetitive manual tasks) and the convenient interaction interface for executing cognitive tasks (e.g., maintenance preparation and guidance job). Hence, a mixed perception-based human-robot collab-orative maintenance approach consisting of three-hierarchy structures is proposed in this paper, which can help reduce the severity of the mentioned problems. In the first stage, a mixed perception module is proposed to help the cobot recognize human safety and maintenance request according to human actions and gestures separately. During the second stage, an improved online deep reinforcement learning (DRL)-enabled decision-making module with the asynchronous structure and the function of anti-disturbance is proposed in this paper, which can realize the execution of robotized maintenance tasks. In the third stage, an augmented reality-assisted (AR) user-friendly interaction interface is designed to help the personnel interact with the cobot and execute the auxiliary maintenance task without the limitation of spatial and human factors. In addition, the auxiliary of maintenance operation can also be supported by the AR-assisted visible guidance. Finally, comparative nu-merical experiments are implemented in a typical machining workshop, and the experimental results show a competitive performance of the proposed HRC maintenance approach compared with other state-of-the-art methods.