An augmented reality-assisted interaction approach using deep reinforcement learning and cloud-edge orchestration for user-friendly robot teaching

Industrial robots have emerged as pivotal components in the search for intelligent manufacturing equipment that can meet flexible and customized operational needs. Consequently, industrial robots have to frequently use motion planning schemes pre-programmed by operators. Furthermore, traditional robot teaching methods in the human-robot interaction scenario can only be applied in a fixed task environment and therefore lack general-ization ability. To address these shortcomings, this research proposes an augmented reality-assisted interaction approach using deep reinforcement learning and cloud-edge orchestration for user-friendly robot teaching. Firstly, the proposed deep reinforcement learning algorithm with the position prediction function is applied for the robot motion planning, which can avoid unnecessary collision attempts during the training process. Sub-sequently, augmented reality glasses provide a user-friendly interaction interface, allowing both virtual and physical robots to be operated to eliminate the limitations of spatial and human factors. Apart from this, the robot target positions can be set by operators, and the visible trajectory of the calculated path can be integrated into the real scenario by virtue of AR glasses. On top of this, the cloud-edge orchestration links the communi-cation between the industrial AR cloud platform and the edge nodes (e.g., robots and augmented reality glasses). Ultimately, comparative numerical experiments are conducted in an actual machining workshop, and the results indicate that the proposed robot teaching approach is both efficient and applicative by virtue of deep rein-forcement learning and cloud-edge orchestration.