The development of flexible swarm robotics systems capable of adapting to the task and environmental changes is a serious challenge. The main motivations of Swarm robotics are decentralized control, stability, adaptivity, and flexibility. Usually, ad-hoc approaches are employed to design a controller capable of meeting the problem specifications. However, these methods cannot be easily verified, and in some cases, it is not even shown that they meet the specifications. Moreover, the controller source code has to be developed separately, primarily when formal methods are employed; As a result, it cannot be guaranteed that the implementation matches the design. This paper proposes a new method - probabilistic timed supervisory control (ptSCT) - to formally design a controller from systems specifications. The proposed ptSCT has several advantages: 1) the automatic generation of the controller source code utilizable in ARGoS platform, 2) formal designing capability using the implemented software tool, 3) set of powerful design components like probabilistic decisions and time constraints, and 4) the reusability of formally designed modules among different scenarios and multiple robotic platforms. Two case studies are considered to investigate various aspects of the proposed system. Firstly, the synchronization case study is implemented for a comparison between SCT and ptSCT in terms of design capabilities and memory consumption. Secondly, the foraging case study as a complex and medium-sized problem is modeled using ptSCT step by step. More than 2400 experiments with a varying number of obstacles, targets, and robots are executed in ARGoS platform in order to show the performance of the automatically generated source code.

能够适应任务和环境变化的柔性群机器人系统的开发是一个严峻的挑战。Swarm机器人技术的主要动机是分散控制，稳定性，适应性和灵活性。通常，采用临时方法来设计能够满足问题规范的控制器。但是，这些方法不容易验证，在某些情况下，甚至没有显示它们符合规范。而且，控制器源代码必须单独开发，主要是在采用形式化方法时；结果，不能保证实现与设计相匹配。本文提出了一种新的方法-概率定时监督控制（ptSCT）-根据系统规范来正式设计控制器。拟议的ptSCT具有以下优点：1）自动生成可在ARGoS平台中使用的控制器源代码； 2）使用已实现的软件工具进行正式设计的能力； 3）一组功能强大的设计组件，如概率决策和时间限制； 4）正式设计模块之间的可重用性不同的场景和多个机器人平台。考虑了两个案例研究来研究提议系统的各个方面。首先，实施同步案例研究，以在设计能力和内存消耗方面比较SCT和ptSCT。其次，使用ptSCT逐步模拟了作为复杂中型问题的觅食案例研究。超过2400个实验，涉及各种障碍，目标，