Multi-Robot AI Swarm (Coordinated Autonomous Fleet)
A multi-robot fleet where specialized robots (Vision Scout, Material Transport, Inspection Rover) coordinate dynamically via a centralized AI fleet orchestrator.
The Problem
What difficult challenge is this experiment tackling?Single robots cannot scale to large physical facilities where concurrent inspection, transport, and monitoring are required simultaneously.
The Hypothesis
What do I believe autonomous AI & engineering can accomplish?Adapting agentic multi-agent swarm algorithms to physical robotics enables autonomous workload division, collision-free fleet routing, and fault-tolerant failovers.
System Architecture & Workflow
End-to-end design, data pipelines, and component interactionBenchmark & Empirical Metrics
Accuracy • Latency • Cost • Token Usage • Human InterventionCoordinates 3 physical prototype rovers in unified mission space without deadlocks; task completion 2.8x faster than solo robot.
Security, Privacy & Ethical Guardrails
Risk mitigation, strict boundaries, and sandbox controlsMutual TLS on robot communication bus; automated return-to-base protocol triggered upon lost network connectivity.
Lessons Learned & Observations
Empirical findings from prototyping and testingDecentralized spatial reservation grids prevent deadlocks when two robots approach narrow hallways simultaneously.
Next Experiment Iteration
Upcoming modifications, scaling factors, or new hypothesesSimulate scaling swarm coordination to 50+ virtual nodes in Gazebo.