Learning and planning for optimal synergistic human-robot coordination in manufacturing contexts
Modern hybrid robot cells leverage heterogeneous agents to provide agile production solutions. Effective agent coordination is crucial to avoid inefficiencies and potential hazards for human operators working among robots. This paper proposes a new human-aware task allocation and scheduling model based on Mixed Integer Non-Linear Programming (MINLP) to optimize efficiency and safety during task planning, scheduling, and allocation. The approach introduces a synergy index that encodes the coupling effects between pairs of tasks executed in parallel by the agents. These terms are learned from previous process executions by means of a Bayesian linear estimation. The task planning model is enhanced by the knowledge of synergy terms to adapt the nominal duration of the plan to consider the effect of the operator's presence. Simulations and experimental results demonstrate the effectiveness of the proposed method in obtaining a proactive human-aware solution starting from the task planning level. The proposed model reduces process execution time and achieves solutions with less agent interference, more considerable human-robot distance, and, thus, safer for agents.