An Integrated Dynamic Method for Allocating Roles and Planning Tasks for Mixed Human-Robot Teams

TL;DR

This paper introduces a dynamic, Behavior Tree-based method for task planning and role allocation in mixed human-robot teams, emphasizing online optimization and simplified sub-problems for manufacturing environments.

Contribution

It presents a novel integrated approach combining Behavior Trees with online optimization to improve task allocation in human-robot teams, reducing complexity compared to traditional methods.

Findings

Effective role allocation in simulations

Reduced computational complexity

Potential for real-world application

Abstract

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.