ビヘイビアツリーを用いた人間とロボットの混在チームのための役割割当とタスク計画の統合動的手法
An Integrated Dynamic Method for Allocating Roles and Planning Tasks for Mixed Human-Robot Teams
製造環境向けに、ビヘイビアツリーで作業を時間・論理制約付きタスクに分解し、混合整数線形計画でオンラインに役割割当を行う手法を提案し、シミュレーションで評価した論文。
著者: Fabio Fusaro, Edoardo Lamon, Elena De Momi, Arash Ajoudani
分類: cs.RO, cs.AI
原文アブストラクト
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.
