Zespol: A Lightweight Environment for Training Swarming Agents
Shay Snyder (1), Kevin Zhu (1), Ricardo Vega (1), Cameron Nowzari (1),, Maryam Parsa (1) ((1) George Mason University)

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…
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Taxonomy
TopicsModular Robots and Swarm Intelligence · Advanced Memory and Neural Computing · Distributed Control Multi-Agent Systems
