Multi-Agent Swarm Coordination for Mapping Non-Euclidean Infrastructure Topologies

Research by Arnav Goel establishes a mathematical framework for multi-agent swarm coordination to map complex infrastructure networks. The study demonstrates a 5.8 to 18.3-fold speed advantage over single-agent approaches through discrete-event simulations in Python using synthetic and real-world-inspired graph datasets. The data supports findings on the efficiency of collective robotics for critical infrastructure assessment.

Use Cases

• Benchmarking decentralized mapping algorithms based on the described communication protocols and decision-making logic.
• Simulating swarm intelligence performance for infrastructure assessment based on the synthetic and real-world-inspired graph datasets.
• Developing local information fusion techniques for topological discovery based on the formalized connected graph environments.

Strengths

• Demonstrates a quantified performance improvement of 5.8 to 18.3 times faster than traditional single-agent approaches.
• Includes simulation results using both synthetic and real-world-inspired graph datasets, suggesting comparative analysis.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for large-scale training.

Provenance

Source

Harvard Dataverse

Collection Method

Generated through discrete-event simulations in Python, as described in the research.

Freshness

Last updated 2026-05-11 15:00:10; freshness should be verified.