Zespol: A Lightweight Environment for Training Swarming Agents

TL;DR

Zespol is a modular, Python-based simulation environment designed to facilitate the development, testing, and scaling of multi-agent control algorithms for robotic swarms, addressing the lack of standardized tools in the field.

Contribution

It introduces Zespol, a flexible and extensible simulation platform that supports both research and real-world applications, including potential integration with neuromorphic computing.

Findings

Zespol accurately replicates existing swarm behaviors in simulation and real-world tests.

The environment demonstrates fidelity in simulating milling behaviors of robotic swarms.

Potential for scaling to complex, real-world swarm scenarios with modular design.

Abstract

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.