SIGMA: An Agent-Based Modeling UAV Swarm Simulator for Swarm Intelligence Algorithms

Abstract

Swarm intelligence for uncrewed aerial vehicles (UAVs) significantly improves the success rate of executing intricate tasks using “distributed platforms and aggregated effects”. However, the high experimental costs and safety risks constrain its development. This paper introduces SIGMA (Swarm Intelligence Generic simulator for Multi-UAVs), a high-fidelity distributed UAV swarm simulator for swarm intelligence algorithms. As an agent-based modeling simulator (ABMS), SIGMA has three key innovations: First, an automatic model tuning method improves aircraft dynamics fidelity. Second, a bidirectional discrete-event simulation (BiDES) architecture resolves the time alignment challenges in distributed systems. Third, a multi-agent learning toolbox ensures algorithm compatibility via an episodic training structure and a memory replay mechanism. In the verification part, the fidelity and scalability of the simulator are verified by quantitative simulations and experiments, and several successful applications demonstrate the practicality of the proposed simulator. Notice to Practitioners—The motivation for this paper stems from the need to develop a scalable and high-fidelity simulator for practical applications of swarm intelligence algorithms. Simulators developed based on game engines are widely used in swarm robotics due to their realistic 3D environments. However, as the number of nodes increases, the real-time performance and scalability of the simulators will decrease significantly. To achieve real-time simulation of large-scale swarms and improve swarm fidelity, SIGMA is proposed. It uses ABMS technology to achieve better performance and can effectively fit the learning tasks of UAV swarms.