Abstract
Swarm robotics explores the coordination of multiple robots to achieve collective goals, with collective decision-making being a central focus. This process involves decentralized robots autonomously making local decisions and communicating them, which influences the overall emergent behavior. Testing such decentralized algorithms in real-world scenarios with hundreds or more robots is often impractical, underscoring the need for effective simulation tools. We propose SPACE (Swarm Planning and Control Evaluation), a Python-based simulator designed to support the research, evaluation, and comparison of decentralized Multi-Robot Task Allocation (MRTA) algorithms. SPACE streamlines core algorithmic development by allowing users to implement decision-making algorithms as Python plug-ins, easily construct agent behavior trees via an intuitive GUI, and leverage built-in support for inter-agent communication and local task awareness. To demonstrate its practical utility, we implement and eval