The growing deployment of human-robot collaborative processes in several
industrial applications, such as handling, welding, and assembly, unfolds the
pursuit of systems which are able to manage large heterogeneous teams and, at
the same time, monitor the execution of complex tasks. In this paper, we
present a novel architecture for dynamic role allocation and collaborative task
planning in a mixed human-robot team of arbitrary size. The architecture
capitalizes on a centralized reactive and modular task-agnostic planning method
based on Behavior Trees (BTs), in charge of actions scheduling, while the
allocation problem is formulated through a Mixed-Integer Linear Program (MILP),
that assigns dynamically individual roles or collaborations to the agents of
the team. Different metrics used as MILP cost allow the architecture to favor
various aspects of the collaboration (e.g. makespan, ergonomics, human
preferences). Human preference are identified through a negotiation phase, in
which, an human agent can accept/refuse to execute the assigned this http URL
addition, bilateral communication between humans and the system is achieved
through an Augmented Reality (AR) custom user interface that provides intuitive
functionalities to assist and coordinate workers in different action phases.
The computational complexity of the proposed methodology outperforms literature
approaches in industrial sized jobs and teams (problems up to 50 actions and 20
agents in the team with collaborations are solved within 1\;s). The different
allocated roles, as the cost functions change, highlights the flexibility of
the architecture to several production requirements. Finally, the subjective
evaluation demonstrating the high usability level and the suitability for the
targeted scenario.

该研究提出了一种基于行为树的动态角色分配和协同任务规划的新型架构，使用混合整数线性规划来指定团队中个体的角色或协作，各种指标作为 MILP 成本允许体系结构支持协作的各个方面，并通过增强现实用户界面实现了人 - 系统之间的双向通信，以协调处理不同操作阶段的工人，并通过主观评估证明了该架构的高可用性和适用性。

混合人机团队中动态角色分配和协同任务规划的综合架构

A Comprehensive Architecture for Dynamic Role Allocation and Collaborative Task Planning in Mixed Human-Robot Teams

Stochastic Programming is a powerful modeling framework for decision-making
under uncertainty. In this work, we tackle two-stage stochastic programs
(2SPs), the most widely used class of stochastic programming models. Solving
2SPs exactly requires optimizing over an expected value function that is
computationally intractable. Having a mixed-integer linear program (MIP) or a
nonlinear program (NLP) in the second stage further aggravates the
intractability, even when specialized algorithms that exploit problem structure
are employed. Finding high-quality (first-stage) solutions -- without
leveraging problem structure -- can be crucial in such settings. We develop
Neur2SP, a new method that approximates the expected value function via a
neural network to obtain a surrogate model that can be solved more efficiently
than the traditional extensive formulation approach. Neur2SP makes no
assumptions about the problem structure, in particular about the second-stage
problem, and can be implemented using an off-the-shelf MIP solver. Our
extensive computational experiments on four benchmark 2SP problem classes with
different structures (containing MIP and NLP second-stage problems) demonstrate
the efficiency (time) and efficacy (solution quality) of Neur2SP. In under 1.66
seconds, Neur2SP finds high-quality solutions across all problems even as the
number of scenarios increases, an ideal property that is difficult to have for
traditional 2SP solution techniques. Namely, the most generic baseline method
typically requires minutes to hours to find solutions of comparable quality.

本文提出了一种名为 Neur2SP 的方法，它通过神经网络近似预期值函数来解决计算上不可行的二阶随机编程模型，同时不假设问题结构并且可以使用现成的 MIP 求解器，并成功应用于四类基准问题。