Learning and planning for optimal synergistic human–robot coordination in manufacturing contexts

Abstract

Collaborative robotics cells leverage heterogeneous agents to provide agile production solutions. Effective coordination is essential to prevent inefficiencies and risks for human operators working alongside robots. This paper proposes a human-aware task allocation and scheduling model based on Mixed Integer Nonlinear Programming to optimize efficiency and safety starting from the task planning stages. The approach exploits synergies that encode the coupling effects between pairs of tasks executed in parallel by the agents, arising from the safety constraints imposed on robot agents. These terms are learned from previous executions using a Bayesian estimation; the inference of the posterior probability distribution of the synergy coefficients is performed using the Markov Chain Monte Carlo method. The synergy enhances task planning by adapting the nominal duration of the plan according to the effect of the operator’s presence. Simulations and experimental results demonstrate that the proposed method produces improved human-aware task plans, reducing useless interference between agents, increasing human–robot distance, and achieving up to an 18% reduction in process execution time. • The paper improves human awareness in task planning for collaborative robotics. • Synergies between parallel tasks are introduced and learned from past executions. • Task durations adjust based on operator presence to enhance planning and coordination. • The robot’s task duration is adjusted through learned synergy terms. • The method is tested in HRC mosaic composition and e-waste disassembly scenarios. • The proactive model improves both efficiency and safety in human-robot collaboration.