Schranz Melanie, Umlauft Martina, Sende Micha, Elmenreich Wilfried
Lakeside Labs GmbH, Klagenfurt, Austria.
Institute of Networked and Embedded Systems, University of Klagenfurt, Klagenfurt, Austria.
Front Robot AI. 2020 Apr 2;7:36. doi: 10.3389/frobt.2020.00036. eCollection 2020.
In swarm robotics multiple robots collectively solve problems by forming advantageous structures and behaviors similar to the ones observed in natural systems, such as swarms of bees, birds, or fish. However, the step to industrial applications has not yet been made successfully. Literature is light on real-world swarm applications that apply actual swarm algorithms. Typically, only parts of swarm algorithms are used which we refer to as basic swarm behaviors. In this paper we collect and categorize these behaviors into spatial organization, navigation, decision making, and miscellaneous. This taxonomy is then applied to categorize a number of existing swarm robotic applications from research and industrial domains. Along with the classification, we give a comprehensive overview of research platforms that can be used for testing and evaluating swarm behavior, systems that are already on the market, and projects that target a specific market. Results from this survey show that swarm robotic applications are still rare today. Many industrial projects still rely on centralized control, and even though a solution with multiple robots is employed, the principal idea of swarm robotics of distributed decision making is neglected. We identified mainly following reasons: First of all, swarm behavior emerging from local interactions is hard to predict and a proof of its eligibility for applications in an industrial context is difficult to provide. Second, current communication architectures often do not match requirements for swarm communication, which often leads to a system with a centralized communication infrastructure. Finally, testing swarms for real industrial applications is an issue, since deployment in a productive environment is typically too risky and simulations of a target system may not be sufficiently accurate. In contrast, the research platforms present a means for transforming swarm robotics solutions from theory to prototype industrial systems.
在群体机器人技术中,多个机器人通过形成类似于自然系统(如蜂群、鸟群或鱼群)中观察到的有利结构和行为来共同解决问题。然而,向工业应用的迈进尚未成功实现。关于应用实际群体算法的现实世界群体应用的文献较少。通常,只使用了群体算法的部分内容,我们将其称为基本群体行为。在本文中,我们收集并将这些行为分类为空间组织、导航、决策和其他类别。然后,这种分类法被应用于对来自研究和工业领域的一些现有群体机器人应用进行分类。除了分类之外,我们还全面概述了可用于测试和评估群体行为的研究平台、已上市的系统以及针对特定市场的项目。本次调查结果表明,如今群体机器人应用仍然很少见。许多工业项目仍然依赖集中控制,即使采用了多机器人解决方案,群体机器人技术中分布式决策的主要理念也被忽视了。我们主要确定了以下原因:首先,局部交互产生的群体行为难以预测,并且难以提供其在工业环境中应用的适用性证明。其次,当前的通信架构往往不符合群体通信的要求,这通常会导致系统具有集中式通信基础设施。最后,对实际工业应用的群体进行测试是一个问题,因为在生产环境中部署通常风险太大,而目标系统的模拟可能不够准确。相比之下,研究平台提供了一种将群体机器人技术解决方案从理论转化为原型工业系统的方法。