Adaptive Workload Allocation for Multi-Human Multi-Robot Teams for Independent and Homogeneous Tasks
Adaptive Workload Allocation for Multi-Human Multi-Robot Teams for Independent and Homogeneous Tasks
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用于独立和同质任务的多人多机器人团队的自适应工作负载分配
DOI：
10.1109/access.2020.3017659
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发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
Min, Byung-Cheol
中科院分区：
文献类型：
--
作者：
Mina, Tamzidul;Kannan, Shyam Sundar;Jo, Wonse;Min, Byung-Cheol
多人多机器人（MH-MR）系统能够将机器人系统的潜在优势与人类参与的优势相结合。机器人系统在重复性任务上提供精确的性能和长时间操作而不疲劳，而人类在循环中提高态势感知并增强决策能力。系统在任务期间根据不断变化的条件和每个人（人类和机器人）的性能调整分配的工作负载的能力对于维持整体系统性能至关重要。以前的文献工作，包括基于市场和优化方法，试图解决任务/工作负载分配问题，重点是最大化系统输出，而不考虑个体代理条件，缺乏实时处理，并且主要集中在多机器人系统上。考虑到团队可能组合的多样性（自主机器人和人工操作机器人：任意数量的操作员同时操作任意数量的机器人）以及 MH-MR 系统的操作规模，开发工作量分配的通用框架一直是一项特别具有挑战性的任务。在本文中，我们提出了这样一个用于独立同质任务的框架，能够根据人类操作和自主机器人的健康状况和工作表现实时自适应地分配系统工作负载。该框架由可拆卸的模块化功能块组成，确保其适用于不同的 MH-MR 场景。新的工作负载转换功能块可确保平稳转换，而工作负载变化不会对各个代理产生不利影响。通过在人类和机器人状况不断变化以及机器人发生故障的 MH-MR 巡逻场景中应用所提出的框架的实验，验证了系统工作负载适应性的有效性和可扩展性。
Multi-human multi-robot (MH-MR) systems have the ability to combine the potential advantages of robotic systems with those of having humans in the loop. Robotic systems contribute precision performance and long operation on repetitive tasks without tiring, while humans in the loop improve situational awareness and enhance decision-making abilities. A system's ability to adapt allocated workload to changing conditions and the performance of each individual (human and robot) during the mission is vital to maintaining overall system performance. Previous works from literature including market-based and optimization approaches have attempted to address the task/workload allocation problem with focus on maximizing the system output without regarding individual agent conditions, lacking in real-time processing and have mostly focused exclusively on multi-robot systems. Given the variety of possible combination of teams (autonomous robots and human-operated robots: any number of human operators operating any number of robots at a time) and the operational scale of MH-MR systems, development of a generalized framework of workload allocation has been a particularly challenging task. In this article, we present such a framework for independent homogeneous missions, capable of adaptively allocating the system workload in relation to health conditions and work performances of human-operated and autonomous robots in real-time. The framework consists of removable modular function blocks ensuring its applicability to different MH-MR scenarios. A new workload transition function block ensures smooth transition without the workload change having adverse effects on individual agents. The effectiveness and scalability of the system's workload adaptability is validated by experiments applying the proposed framework in a MH-MR patrolling scenario with changing human and robot condition, and failing robots.
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