St-Onge David, Varadharajan Vivek Shankar, Švogor Ivan, Beltrame Giovanni
INIT Robots Laboratory, Department of Mechanical Engineering, École de technologie supérieure, Montreal, QC, Canada.
MIST Laboratory, Department of Computer Engineering and Software Engineering, Polytechnique Montreal, Montreal, QC, Canada.
Front Robot AI. 2020 May 7;7:51. doi: 10.3389/frobt.2020.00051. eCollection 2020.
Many applications benefit from the use of multiple robots, but their scalability and applicability are fundamentally limited when relying on a central control station. Getting beyond the centralized approach can increase the complexity of the embedded software, the sensitivity to the network topology, and render the deployment on physical devices tedious and error-prone. This work introduces a software-based solution to cope with these challenges on commercial hardware. We bring together our previous work on Buzz, the swarm-oriented programming language, and the many contributions of the Robotic Operating System (ROS) community into a reliable workflow, from rapid prototyping of decentralized behaviors up to robust field deployment. The Buzz programming language is a hardware independent, domain-specific (swarm-oriented), and composable language. From simulation to the field, a Buzz script can stay unmodified and almost seamlessly applicable to all units of a heterogeneous robotic team. We present the software structure of our solution, and the swarm-oriented paradigms it encompasses. While the design of a new behavior can be achieved on a lightweight simulator, we show how our security mechanisms enhance field deployment robustness. In addition, developers can update their scripts in the field using a safe software release mechanism. Integrating Buzz in ROS, adding safety mechanisms and granting field updates are core contributions essential to swarm robotics deployment: from simulation to the field. We show the applicability of our work with the implementation of two practical decentralized scenarios: a robust generic task allocation strategy and an optimized area coverage algorithm. Both behaviors are explained and tested with simulations, then experimented with heterogeneous ground-and-air robotic teams.
许多应用都受益于多机器人的使用，但当依赖中央控制站时，它们的可扩展性和适用性从根本上受到限制。超越集中式方法可能会增加嵌入式软件的复杂性、对网络拓扑的敏感性，并使在物理设备上的部署变得繁琐且容易出错。这项工作引入了一种基于软件的解决方案，以应对商业硬件上的这些挑战。我们将之前在面向群体的编程语言Buzz方面的工作，以及机器人操作系统（ROS）社区的众多贡献整合到一个可靠的工作流程中，从分散行为的快速原型设计到稳健的现场部署。Buzz编程语言是一种与硬件无关、特定领域（面向群体）且可组合的语言。从模拟到现场，一个Buzz脚本可以保持不变，并且几乎可以无缝应用于异构机器人团队的所有单元。我们展示了我们解决方案的软件结构，以及它所包含的面向群体的范式。虽然可以在轻量级模拟器上实现新行为的设计，但我们展示了我们的安全机制如何增强现场部署的稳健性。此外，开发人员可以使用安全的软件发布机制在现场更新他们的脚本。将Buzz集成到ROS中、添加安全机制并允许现场更新是群体机器人部署从模拟到现场的核心关键贡献。我们通过两个实际的分散场景的实现展示了我们工作的适用性：一个稳健的通用任务分配策略和一个优化的区域覆盖算法。这两种行为都通过模拟进行了解释和测试，然后在异构的地面和空中机器人团队上进行了实验。
