English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

This paper proposes a novel dynamic method based on Behavior Trees (BTs) that integrates planning and allocation of 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 formulating an offline centralized optimization problem, the role allocation problem is solved with multiple simplified online optimization sub-problems, without complex and cross-schedule task dependencies. These sub-problems are defined as Mixed-Integer Linear Programs (MILPs), that, according to the worker-actions related costs and the workers&#x2019; 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 the computational complexity are evaluated. The obtained results, due to the nature of the algorithm and to the possibility of simulating the agents&#x2019; behavior, illustrate adequately also how the algorithm performs in real experiments.

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