The challenges of deploying robots and autonomous vehicles call for further efforts to bridge the gap between the robotics, the real-time systems and the formal methods communities. Indeed, with robots being more and more involved in costly missions and contact with humans, a rigorous formal verification of their behavior in the presence of various real-time constraints is crucial. In order to increase our trust in its results, such verification should be carried out on models that are as close as possible to reality, and thus take into account hardware and OS specificities such as the number of cores provided by the robotic platform and the scheduling policy. In this paper, we propose a novel binary-search-inspired technique that allows to extend timed automata models of robotic specifications with dynamic-priority schedulers. Given a number of cores, the extended models can then be checked against various real-time and behavioral properties, including schedulability, within the same model checking framework. Our technique is implemented in an automatic translation from a robotic framework to UPPAAL, and evaluated on a real robotic case study, where it shows a significant gain in scalability as opposed to the counting technique used in the literature.

部署机器人和自动驾驶汽车的挑战要求进一步努力弥合机器人技术，实时系统和形式方法社区之间的鸿沟。实际上，随着机器人越来越多地参与昂贵的任务并与人类接触，在各种实时约束条件下对其行为进行严格的形式验证至关重要。为了增加我们对其结果的信任，应该在尽可能接近实际的模型上进行这种验证，并因此考虑到硬件和操作系统的特殊性，例如机器人平台提供的内核数量和调度政策。在本文中，我们提出了一种新颖的二进制搜索启发技术允许使用动态优先级调度程序扩展机器人规格的定时自动机模型。给定多个内核，然后可以在同一模型检查框架内针对各种实时和行为属性（包括可调度性）检查扩展模型。我们的技术在从机器人框架到UPPAAL的自动转换中实现，并在真实的机器人案例研究中进行了评估，与在文献中使用的计数技术相比，它在可伸缩性方面显示出显着的优势。