Early prediction of turn-taking based on spiking neuron network to facilitate human-robot collaborative assembly

Abstract

In the context of industry 5.0, human-robot collaboration (HRC) in assembly, a flexible production mode, has been paid increasing attention. In the scenario of HRC in assembly, to perfect the efficiency and fluency of the whole assembly process, the leading point is to develop a more natural human-robot interaction (HRI). In that way, the robot has the access to predict the human's intention earlier. The single process's intention has been mainly focused on human intention prediction, however, is verified against the natural HRI, causing the robot insensitive to turn-taking among the successive process. Therefore, this paper enters a proposal that we can realize early prediction of turn-taking in HRC assembly tasks based on Izhikevich neuron model-based spiking neuron network (SNN). The proposal is also verified in a developed HRC gear assembly scenario. The results express that our method can greatly advance the recognition time of human-robot turn-taking, which improves the efficiency of human-robot collaborative assembly.