This paper proposes a novel integrated dynamic method based on Behavior Trees for planning and allocating tasks in mixed human robot teams, suitable for manufacturing environments. The Behavior Tree formulation allows encoding a single job as a compound of different tasks with temporal and logic constraints. In this way, instead of the well-studied offline centralized optimization problem, the role allocation problem is solved with multiple simplified online optimization sub-problem, without complex and cross-schedule task dependencies. These sub-problems are defined as Mixed-Integer Linear Programs, that, according to the worker-actions related costs and the workers' availability, allocate the yet-to-execute tasks among the available workers. To characterize the behavior of the developed method, we opted to perform different simulation experiments in which the results of the action-worker allocation and computational complexity are evaluated. The obtained results, due to the nature of the algorithm and to the possibility of simulating the agents' behavior, should describe well also how the algorithm performs in real experiments.
翻译:本文提出了基于行为树的新颖的综合动态方法,用于规划和分配适合制造环境的混合人类机器人团队的任务。行为树配制允许将单一的工作编码为具有时间和逻辑限制的不同任务组合。这样,我们选择了不同的模拟实验,评估行动工人分配和计算复杂性的结果。由于算法的性质以及模拟代理人行为的可能性,所获得的结果也应很好地说明算法在实际实验中是如何进行的。