GG Swarm
A decentralized coordination framework transitioning from simulation to real-world drone light shows.
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
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2026-09-03
The long way around: I finally finished the computer science degree
I failed a programming course in high school and decided it wasn't for me. Fourteen years and two other degrees later, I finished a BS in CS while working full-time. -
2026-05-01
Introducing GG Swarm Live: From Simulation to the Sky
With the ink finally dry on my Computer Science degree from CSUMB, I’m excited to officially announce the next chapter of my work in autonomous robotics: GG Swarm Live.
During my capstone, I focused heavily on the “brains” of the operation—developing a decentralized coordination framework using Graph Attention Networks (GATv2) and Reinforcement Learning (PPO). While the simulation results in NVIDIA Isaac Lab were promising, the ultimate goal has always been to see these algorithms control physical hardware in the real world.
The Hardware Migration
GG Swarm Live is a dedicated program to transition my research from the “digital twin” to real-world airframes. This isn’t just about porting code; it’s about handling the messy realities of physics, communication latencies, and sensor noise that simulation can only approximate.
The technical stack for this phase is built on industry-standard foundations:
- PX4 Autopilot: The core flight stack for our physical airframes.
- Skybrush: We are integrating our RL-based execution layer with Skybrush to allow for expressive, choreographed drone light shows.
- Crazyflie: Initial sim-to-real baselining will take place on these micro-drones in a controlled indoor environment before scaling to larger outdoor platforms.
Beyond the Capstone
The capstone gave us a v1.0.0 baseline. The roadmap for GG Swarm Live expands this into eight distinct phases, ranging from multi-drone shared-scene training to fully autonomous, obstacle-aware navigation in outdoor environments.
One of the most exciting commercial applications we’re exploring is the “backyard light show” capability. By combining decentralized swarm logic with a user-friendly interface, we hope to make high-end aerial displays accessible for local events and creative displays. Crucially, the decentralized nature of the swarm adds a robust layer of safety; by removing single points of failure, we can ensure more reliable behavior around spectators and audience members.
Follow the Journey
I’ve updated the GG Swarm Project Page to serve as the central hub for this new phase. It now includes the full hardware roadmap and a section archiving the original academic research.
This degree wasn’t a transition to becoming an engineer—I’ve been one since 2012—but rather a significant upgrade to my toolkit. The build is just beginning. Stay tuned for more logs as we take these swarms to the sky.
Gary
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2026-04-24
Week 16 — Capstone Festival, Program Complete
Last entry. Capstone Festival is today, portfolio and Learning Journal are submitted, and
v1.0.0-capstoneis tagged. CSUMB is done. -
2026-04-21
Week 15 — Project Wrapped, Testing Report Locked, Draft Presentation Cut
Project wrapped for the Capstone Festival. Testing Report closed out, draft presentation video recorded against the Phase 5 cinematic clips, and the README got a final polish including an embedded 8-drone highlight GIF. 3 days remain to the Apr 24 deadline — remaining work is rehearsal, portfolio/journal submission, and the
v1.0.0-capstonetag. -
2026-04-14
Week 14 — Phase 5 Complete, Phase 6 in Flight, Thesis Clarified
Phase 5 done 12 days ahead of the M4 gate, Phase 6 underway. The cinematic trailer was edited from the 20 captured clips and published to YouTube early in the week. The back half shifted to documentation and framing — making the research question explicit: coordination, not stabilization. 10 days remain to the Apr 24 deadline. No training is planned; only editing, rehearsal, and submission remain.
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2026-04-07
Week 13 — Phase 4 Complete, Showcase Prep Begins 7 Days Early
Forest navigation now works cleanly — 0 body penetrations in 700-step play with 8 drones weaving through 40cm tree trunks. Phase 4 is complete and Phase 5 (Showcase Prep) starts 7 days early.
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2026-03-31
Week 12 — Phase 3 Complete, 9 Days Early
Rebuilt Phase 2 from scratch, achieved formation control in a single session, then blew through Phase 3 — finishing 9 days ahead of schedule.
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2026-03-24
Week 11 — Phase 2 Complete, Formation Control Achieved
Completed Phase 2 — drones can now maintain stability and hold formation distance from each other.
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2026-03-17
Week 10 — MAPPO Training Pipeline and Environment Finalization
Finalized the MARL environment, set up the MAPPO training pipeline, and replanned the schedule after falling ~2 weeks behind.
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2026-03-10
Week 9 — Isaac Lab Integration and Brain Development
Set up the Isaac Lab development environment locally and got my first Crazyflie drone running in simulation — laying the groundwork for multi-agent training.
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2026-02-11
Project Launch: GG Swarm - Decentralized UAV Coordination
This post introduces the GG Swarm capstone project, a decentralized coordination framework designed to overcome the single point of failure and high latency of centralized control in large UAV swarms. By integrating Graph Neural Networks (GNNs) for spatial reasoning with advanced Minimum Control (MINCO) trajectory optimization, the system achieves robust, fault-tolerant behavior for high-stakes applications.