Agent-based modeling (ABM) and simulation have emerged as important tools for studying emergent behaviors, especially in the context of swarming algorithms for robotic systems. Despite significant research in this area, there is a lack of standardized simulation environments, which hinders the development and deployment of real-world robotic swarms. To address this issue, we present Zespol, a modular, Python-based simulation environment that enables the development and testing of multi-agent control algorithms. Zespol provides a flexible and extensible sandbox for initial research, with the potential for scaling to real-world applications. We provide a topological overview of the system and detailed descriptions of its plug-and-play elements. We demonstrate the fidelity of Zespol in simulated and real-word robotics by replicating existing works highlighting the simulation to real gap with the milling behavior. We plan to leverage Zespol's plug-and-play feature for neuromorphic computing in swarming scenarios, which involves using the modules in Zespol to simulate the behavior of neurons and their connections as synapses. This will enable optimizing and studying the emergent behavior of swarm systems in complex environments. Our goal is to gain a better understanding of the interplay between environmental factors and neural-like computations in swarming systems.

翻译：基于智能体的建模与仿真已成为研究涌现行为的重要工具，尤其是在机器人系统的集群算法领域。尽管该领域研究成果丰硕，但缺乏标准化的仿真环境，这阻碍了真实世界机器人集群的开发与部署。为解决这一问题，我们提出Zespol——一个基于Python的模块化仿真环境，支持多智能体控制算法的开发与测试。Zespol为初步研究提供了灵活可扩展的沙盒环境，并具备向真实应用场景扩展的潜力。本文给出了系统的拓扑概览，并详细描述了其即插即用组件。通过复现现有研究中的研磨行为以验证仿真与现实之间的差距，我们证明了Zespol在仿真和真实机器人中的保真度。我们计划利用Zespol的即插即用特性开展集群场景中的神经形态计算研究，即使用Zespol中的模块模拟神经元及其突触连接行为。这将有助于优化并研究复杂环境下集群系统的涌现行为。我们的目标是更深入地理解集群系统中环境因素与类神经计算之间的相互作用。