Optimal scheduling of human–robot collaborative assembly operations with time Petri nets

The novel paradigm of collaborative automation, with machines and industrial robots that synergically share the same workspace with human workers, requires to rethink how activities are prioritized in order to account for possible variabilities in their durations. This article proposes a scheduling method for collaborative assembly tasks that allows to optimally plan assembly activities based on the knowledge acquired during runtime and so adapts to variations along the life cycle of a manufacturing process. The scheduler is based on time Petri nets and the output plan is optimized by minimizing the idle time of each agent. The experimental validation carried out on a realistic industrial use-case consisting of a small assembly line with two robots and a human operator confirms the effectiveness of the approach.