Edge-to-Cloud Swarm Coordination for autonomous urban air mobility routing under real-time policy constraints

Description and use

An edge‑to‑cloud hierarchy distributes swarm coordination across cloud, edge, and vehicle tiers, enabling real‑time policy compliance and eliminating cascading failures of centralized planners.

Problem

Coordinating thousands of autonomous drones in urban airspace while complying with dynamic, location‑specific policy constraints that can change in milliseconds.

Outcome

The edge‑to‑cloud system reduced coordination latency to sub‑second for regional clusters and eliminated cascading failures, enabling safe routing of large drone swarms under rapidly changing airspace policies.

Workflow

• 1. Deploy a three‑tier architecture: cloud, edge, vehicle.
• 2. Implement Raft consensus on edge nodes to share local constraints.
• 3. Use a policy‑constrained PPO model on each drone to map observations to actions while filtering against real‑time policy constraints.
• 4. Continuously update global policy plans in the cloud and propagate to edge clusters.
• 5. Validate coordination in a simulated swarm of 10,000 drones on a home lab with RTX 4090s and Raspberry Pi 5s.

Evidence

I watched in my simulations as a single network partition caused 3,000 simulated drones to enter emergency landing procedures simultaneously over a densely populated area.

Source

Through studying distributed systems theory and applying it to aerial robotics, I developed a hierarchical coordination framework that distributes intelligence across three tiers.

Source