Automated Extraction and Checking of Property Models from Source Code for Robot Swarms

E. Merlo, Carlo Pinciroli, Jacopo Panerati, Michalis Famelis, G. Beltrame
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Abstract

As robots become a common presence in our everyday lives, ensuring the security and safety of robotic systems becomes an increasingly important and urgent challenge. Multi-robot systems, in particular, have the potential to revolutionize multiple industries-such as transportation and home care-where safety guarantees are a primary requirement. A known challenge for swarms and multi-robot systems is the gap between requirements and design, due to the need to translate swarm-level objectives into robot-level behaviors. In this paper, we focus on a less studied problem-the gap between requirements and implementation. As a case study, we use Buzz, that is a dynamic programming language designed for swarm robotics applications. Similarly to Python, Lua, and JavaScript, Buzz does not natively offer formal guarantees of correctness or safety. We propose an approach to automatically extract” as-implemented” models from Buzz programs, whose properties can then be formally analyzed and verified. Results obtained from the experiments performed on two medium-size open-source production-level systems for robotics research have also been reported. Our results show that the approach is feasible and is scalable to larger systems.
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基于源代码的机器人群属性模型的自动提取与检验
随着机器人在我们的日常生活中变得越来越普遍,确保机器人系统的安全成为一个越来越重要和紧迫的挑战。特别是多机器人系统,有可能彻底改变多个行业,比如交通运输和家庭护理,在这些行业中,安全保障是首要要求。对于群体和多机器人系统来说,一个已知的挑战是需求和设计之间的差距,因为需要将群体级别的目标转化为机器人级别的行为。在本文中,我们关注的是一个较少被研究的问题——需求和实现之间的差距。作为一个案例研究,我们使用Buzz,这是一种为群体机器人应用程序设计的动态编程语言。与Python、Lua和JavaScript类似,Buzz本身并不提供正确性或安全性的正式保证。我们提出了一种从Buzz程序中自动提取“已实现”模型的方法,然后可以对其属性进行正式分析和验证。还报道了在两个中型开源生产级机器人研究系统上进行的实验结果。我们的结果表明,该方法是可行的,并可扩展到更大的系统。
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