  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.
