SIGMA: An Agent-Based Modeling UAV Swarm Simulator for Swarm Intelligence Algorithms
Sheng Zhang, Juan Li, Chang Liu, Lei Fu, Zehao Bai, Jie Li
AI summary
Problem
High costs and safety risks limit the development of UAV swarm intelligence algorithms, while existing simulators struggle to balance scalability, physical fidelity, and real-time performance for large distributed systems.
Approach
The authors propose SIGMA, a distributed agent-based simulator that uses a hybrid peer-to-peer architecture, automatic aerodynamic parameter tuning, and bidirectional discrete-event simulation to synchronize large-scale UAV swarms in real time.
Key results
- Distributed ABMS architecture with hybrid P2P networking supports 200–500 concurrent nodes
- Automatic model tuning method improves aircraft dynamics fidelity using flight log data
- BiDES time alignment resolves synchronization delays in distributed swarm simulations
- Multi-agent learning toolbox enables episodic training and experience replay for algorithm compatibility
Why it matters
Provides researchers and engineers with a scalable, high-fidelity platform to safely train and validate swarm intelligence algorithms before costly real-world deployment.
Abstract
Swarm intelligence for uncrewed aerial ve- hicles (UAVs) significantly improves the success rate of executing intricate tasks using “distributed platforms and aggregated effects”. However, the high experi- mental costs and safety risks constrain its develop- ment. This paper introduces SIGMA (Swarm Intel- ligence 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 innova- tions: First, an automatic model tuning method im- proves aircraft dynamics fidelity. Second, a bidirec- tional 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 struc- ture and a memory replay mechanism. In the verifica- tion part, the fidelity and scalability of the simulator are verified by quantitative simulations and experi- ments, and several successful applications demonstrate the practicality of the proposed simulator. Notice to Practitioners—The motivation for this pa- per 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 perfor- mance 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.