GG Swarm

A decentralized coordination framework transitioning from simulation to real-world drone light shows.

GG Swarm Hero

Mission Objective

Deploy an adaptive RL-based execution layer onto physical PX4-based drone swarms for commercial light shows and complex formation maneuvers.

Current Phase: GG Swarm Live (Post-Capstone)

Following the successful completion of the academic capstone, the project has transitioned into GG Swarm Live, a real-hardware deployment program. The focus is now on taking the decentralized GATv2/PPO policies developed in simulation and deploying them onto physical PX4-based airframes. This creates an adaptive execution layer that can handle formation stability, obstacle avoidance, and decentralized coordination for commercial drone light shows.

Roadmap & Development Phases

The project is currently progressing through the following hardware-focused roadmap:

Phase Title Status Details
0 Capstone Baseline ✅ Complete v1.0.0-capstone simulation baseline.
1 Shared-Scene Training 🔨 Active Multi-drone training in complex shared simulation scenes.
2 Sim-to-Real Baseline 📅 Planned Initial deployment to Crazyflie drones with LPS.
3 Decentralized Assignment 📅 Planned Transitioning to peer-to-peer ranging and consensus logic.
4 Drone Show Capability 📅 Planned Integration with Skybrush for expressive shapes and light shows.
5 Outdoor Deployment 📅 Planned Extended fault tolerance and RTK-GPS integration.
6 Onboard Compute 📅 Planned Moving all inference and obstacle avoidance to onboard chips.
7 Hardware-Agnostic 🚀 Stretch General-purpose adaptive swarm execution layer.

Archived Milestone: Academic Capstone (v1.0.0)

This section preserves the original research and simulation work completed for my Computer Science Capstone at CSUMB.

Project Overview

The capstone addressed a critical bottleneck in the deployment of unmanned aerial vehicle swarms by tackling the inherent vulnerabilities found in centralized control architectures. The project proposed a fully decentralized coordination framework where global formation behavior emerges naturally from local agent interactions.

Technical Architecture

The architecture was split into the Brain (GATv2 spatial reasoning) and the Muscles (MINCO trajectory optimization), unified by a GNSC 5-Layer model.

GNSC 5-Layer Architecture

flowchart BT
    L1["<b>Layer 1: Local Sensing</b><br/>12D body-frame + K×3 neighbor rel_pos"]
    L2["<b>Layer 2: GNN Message Passing</b><br/>2-layer GATv2, K=2 sparse edges, edge cache"]
    L3["<b>Layer 3: Distributed Consensus</b><br/>MINCO min-jerk filter (T=0.04s) + SwarmRaft dropout"]
    L4["<b>Layer 4: Runtime Safety Shields</b><br/>CBF barrier constraints, clamped corrections, MINCO sync"]
    L5["<b>Layer 5: Mission Execution</b><br/>Thrust/moment mapping → physics"]

    L1 --> L2 --> L3 --> L4 --> L5

    style L1 fill:#3498db,color:#fff
    style L2 fill:#2ecc71,color:#fff
    style L3 fill:#f39c12,color:#fff
    style L4 fill:#e74c3c,color:#fff
    style L5 fill:#8e44ad,color:#fff

Capstone Timeline & Simulation Performance

The simulation phase leveraged NVIDIA Isaac Lab for GPU-accelerated physics, achieving high-fidelity results in formation stability and obstacle avoidance.

  • Mean Formation Error: < 0.1m during steady flight.
  • Success Rate: > 95% across randomized obstacle-dense environments.

Capstone Timeline

  • Weeks 1–4: Proposal and Plan (Completed)
  • Weeks 5–11: Core Development (Brain/Muscles) (Completed)
  • Weeks 12–15: Stress Testing and Showcase Prep (Completed)
  • Week 16: Capstone Festival Delivery (Completed April 2026)

Original Simulation Demo

Project Log

Tech Stack

Python PyTorch PX4 Autopilot Skybrush NVIDIA Isaac Lab GATv2 PPO Crazyflie

Deployment

Timeline

Research

Concept & Requirements

Design

Architecture & Prototyping

Build

Implementation & Refinement

Test

Verification & Validation