Robotic swarms are usually designed in a bottom-up way, which can make robotic swarms vulnerable to environmental impact. It is particularly true for the widely used control mode of robotic swarms, where it is often the case that neither the correctness of the swarming tasks at the macro level nor the safety of the interaction among agents at the micro level can be guaranteed. To ensure that the behaviors are safe at runtime, it is necessary to take into account the property guard approaches for robotic swarms in uncertain environments. Runtime enforcement is an approach which can guarantee the given properties in system execution and has no scalability issue. Although some runtime enforcement methods have been studied and applied in different domains, they cannot effectively solve the problem of property enforcement on robotic swarm tasks at present. In this paper, an enforcement method is proposed on swarms which should satisfy multi-level properties in uncertain environments. We introduce a macro-micro property enforcing framework with the notion of agent shields and a discrete-time enforcing mechanism called D-time enforcing. To realize this method, a domain specification language and the corresponding enforcer synthesis algorithms are developed. We then apply the approach to enforce the properties of the simulated robotic swarm in the robotflocksim platform. We evaluate and show the effectiveness of the method with experiments on specific unmanned aerial vehicle swarm tasks.

机器人群通常以自下而上的方式进行设计，这会使机器人群易受环境影响。对于广泛使用的机器人群体控制模式尤其如此，在这种情况下，通常既不能保证在宏观水平上的蜂拥任务的正确性，也不能保证微观上的代理之间交互的安全性。为了确保行为在运行时是安全的，有必要考虑不确定环境中机器人群体的财产保护方法。运行时强制执行是一种可以保证系统执行中给定属性并且没有可伸缩性问题的方法。尽管已经研究了一些运行时实施方法并将其应用于不同领域，他们目前无法有效解决机器人群体任务的财产强制执行问题。本文提出了一种在不确定环境下应满足多级属性的群体执行方法。我们介绍了具有代理屏蔽概念的宏观-微观属性执行框架和称为“离散时间”的执行机制D时间执行。为了实现该方法，开发了领域规范语言和相应的强制执行器综合算法。然后，我们应用该方法在robotflocksim平台中强制执行模拟机器人群的属性。我们通过对特定的无人机群任务进行实验来评估并显示该方法的有效性。