Swarm Simulation
Provide a reproducible test environment for uncrewed-system concept assessment and algorithm comparisons.
New Horizon provides uncrewed swarm simulation services, delivering scenarios, strategy comparison conditions, and evaluation reports, with integration testing available for AI agent algorithms.
Last updated: 2026-08-12 · New Horizon Digital Outsourcing Team
Explore implementation guides and case studies to understand input requirements, technical approaches, delivered assets, and methods for evaluating outcomes.
Engineering Notes
Define agent counts, initial distributions, motion constraints, communication ranges, and resource budgets. Select specific tasks such as coverage, inspection, or transport, and establish shared criteria for task completion and failure.
Use the same scenario sets and random seeds to compare task completion rates, elapsed time, energy consumption, and conflict events. Introduce disturbances such as communication interruptions, individual agent departures, and sensor noise, retaining complete run records.
Summarize average results, variability, and extreme failure cases separately. Remeasure real-time performance and resource usage when system scale changes; do not infer unlimited scalability from results with a small number of agents.
Related standards and technical documentation for solution selection and implementation.
Describe your stage objectives and constraints (without classified details), and we will provide scope recommendations and a deliverables list.
