Turn-Taking Prediction for Human–Robot Collaborative Assembly Considering Human Uncertainty

Abstract

Abstract Human–robot collaboration (HRC) combines the repeatability and strength of robots and human’s ability of cognition and planning to enable a flexible and efficient production mode. The ideal HRC process is that robots can smoothly assist workers in complex environments. This means that robots need to know the process’s turn-taking earlier, adapt to the operating habits of different workers, and make reasonable plans in advance to improve the fluency of HRC. However, many of the current HRC systems ignore the fluent turn-taking between robots and humans, which results in unsatisfactory HRC and affects productivity. Moreover, there are uncertainties in humans as different humans have different operating proficiency, resulting in different operating speeds. This requires the robots to be able to make early predictions of turn-taking even when human is uncertain. Therefore, in this paper, an early turn-taking prediction method in HRC assembly tasks with Izhi neuron model-based spiking neural networks (SNNs) is proposed. On this basis, dynamic motion primitives (DMP) are used to establish trajectory templates at different operating speeds. The length of the sequence sent to the SNN network is judged by the matching degree between the observed data and the template, so as to adjust to human uncertainty. The proposed method is verified by the gear assembly case. The results show that our method can shorten the human–robot turn-taking recognition time under human uncertainty.