The Industry 4.0 paradigm promises shorter development times, increased ergonomy, higher flexibility and resource efficiency in manufacturing environments. Collaborative robots are an important tangible technology for implementing such a paradigm. A major bottleneck to effectively deploy collaborative robots to manufacturing industries is developing task planning algorithms that enable them to recognize and naturally adapt to varying and even unpredictable human actions while simultaneously ensuring an overall efficiency in terms of production cycle time. In this context, an architecture encompassing task representation, task planning, sensing and robot control has been designed, developed and evaluated in a real industrial environment. A pick-and-place palletization task, which requires the collaboration between humans and robots, is investigated. The architecture uses AND/OR graphs for representing and reasoning upon human–robot collaboration models online. Furthermore, objective measures of the overall computational performance and subjective measures of naturalness in human–robot collaboration have been evaluated by performing experiments with production-line operators. The results of this user study demonstrate how human–robot collaboration models like the one we propose can leverage the flexibility and the comfort of operators in the workplace. In this regard, an extensive comparison study among recent models has been carried out.

工业4.0范式承诺在制造环境中缩短开发时间，增加人体工程学，提高灵活性并提高资源效率。协作机器人是实现这种范式的重要有形技术。有效地将协作机器人部署到制造业的主要瓶颈是制定任务计划这些算法使他们能够识别并自然地适应变化甚至是不可预测的人类行为，同时确保生产周期的整体效率。在这种情况下，已经在实际的工业环境中设计，开发和评估了包含任务表示，任务计划，传感和机器人控制的体系结构。研究了需要人与机器人协作的拾取和放置码垛任务。该体系结构使用AND / OR图来在线表示和推理人机协作模型。此外，通过与生产线操作员进行实验，评估了人机协作中总体计算性能的客观度量和自然性的主观度量。这项用户研究的结果表明，我们建议的人机协作模型如何利用工作场所操作员的灵活性和舒适性。在这方面，最近的模型之间进行了广泛的比较研究。