SPACE: A Python-based Simulator for Evaluating Decentralized Multi-Robot Task Allocation Algorithms

TL;DR

SPACE is a Python-based simulation tool that enables researchers to evaluate and compare decentralized multi-robot task allocation algorithms efficiently, supporting development, testing, and analysis in swarm robotics.

Contribution

The paper introduces SPACE, a novel, user-friendly Python simulator that facilitates the development and evaluation of decentralized MRTA algorithms with customizable decision-making and communication features.

Findings

SPACE effectively supports implementation of algorithms like CBBA and GRAPE.

The simulator enables performance comparison across dynamic task scenarios.

SPACE demonstrates its utility through rigorous evaluation of MRTA algorithms.

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 evaluate CBBA and GRAPE within the simulator, comparing their performance across different metrics, particularly in scenarios with dynamically introduced tasks. This evaluation shows the usefulness of SPACE in conducting rigorous and standardized comparisons of MRTA algorithms, helping to support future research in the field.