With the substantial growth of logistics businesses the need for larger and more automated warehouses increases, thus giving rise to fully robotized shop-floors with mobile robots in charge of transporting and distributing goods. However, even in fully automatized warehouse systems the need for human intervention frequently arises, whether because of maintenance or because of fulfilling specific orders, thus bringing mobile robots and humans ever closer in an integrated warehouse environment. In order to ensure smooth and efficient operation of such a warehouse, paths of both robots and humans need to be carefully planned; however, due to the possibility of humans deviating from the assigned path, this becomes an even more challenging task. Given that, the supervising system should be able to recognize human intentions and its alternative paths in real-time. In this paper, we propose a framework for human deviation detection and intention recognition which outputs the most probable paths of the humans workers and the planner that acts accordingly by replanning for robots to move out of the human's path. Experimental results demonstrate that the proposed framework increases total number of deliveries, especially human deliveries, and reduces human-robot encounters.

中文翻译：

---

集成仓库系统中人类意识规划的人类意图识别

随着物流业务的大幅增长，对更大、更自动化的仓库的需求增加，从而产生了完全机器人化的车间，并配备了负责运输和分配货物的移动机器人。然而，即使在完全自动化的仓库系统中，也经常需要人工干预，无论是因为维护还是因为完成特定订单，从而使移动机器人和人类在集成仓库环境中更加接近。为了保证这样一个仓库的平稳高效运行，机器人和人类的路径都需要精心规划；然而，由于人类可能会偏离指定的路径，这将成为一项更具挑战性的任务。鉴于，监管系统应该能够实时识别人类意图及其替代路径。在本文中，我们提出了一个用于人类偏差检测和意图识别的框架，该框架输出人类工人最可能的路径，并通过重新规划机器人移出人类路径来相应地采取行动。实验结果表明，所提出的框架增加了交付的总数，尤其是人工交付，并减少了人机相遇。