LawnTech Dynamics is an innovative multi-floor racquet sports facility concept combining autonomous operations, sustainable grass cultivation, and AI-powered performance analytics. Located in Naperville, Illinois, this facility aims to revolutionize indoor sports through cutting-edge technology and sustainable practices.

• 🌱 Autonomous Grass Management: Vertical farming system that grows and swaps court surfaces robotically
• 🤖 AI-Powered Operations: Self-managing facility with minimal human oversight
• 📊 Performance Analytics: Real-time biomechanics tracking and injury prevention
• ♻️ Sustainable Design: Solar-powered with advanced resource optimization

24 premium tennis courts featuring:

• Grass courts (replaceable modular turf)
• Hard courts
• Clay courts
• Wood courts
• Pro shop and premium locker rooms

• 16 badminton courts
• 4 squash courts
• 16 table tennis stations

• 8 pickleball courts
• 1 historic real tennis court

2,000 m² autonomous farming facility with:

• Hydroponics and climate control
• Robotic patch transport system
• 60-minute court surface replacement capability

• React 19 with TypeScript
• Framer Motion for animations
• Three.js for 3D facility visualization
• Tailwind CSS for styling
• Vite for build tooling

• Google Gemini AI for chat interface
• Computer vision for biomechanics analysis
• Predictive maintenance algorithms

• Building Management System (BMS)
• Robotic mowers and maintenance drones
• Biometric access control
• Smart HVAC optimization

• Node.js (v20 or higher)
• npm or yarn
• Git

1. Clone the repository

   git clone https://github.com/kvnloo/ace.git
   cd ace

2. Install dependencies

   npm install

3. Configure environment Create a .env.local file:

   GEMINI_API_KEY=your_api_key_here

4. Start development server

   npm run dev

   Open http://localhost:3000 in your browser

npm run build
npm run preview

This project uses GitHub Actions for automated deployment to GitHub Pages:

• Production: Automatically deploys from main branch to /
• Development: Automatically deploys from dev branch to /dev/

See Deployment Guide for detailed setup instructions.

• Production: https://kvnloo.github.io/ace/
• Development: https://kvnloo.github.io/ace/dev/

Explore the entire facility complex with:

• Rotatable 3D visualization
• Interactive hotspots for each area
• Detailed feature information cards

Get instant answers about:

• Facility features and amenities
• Membership options
• Technical specifications
• Court booking

• Biomechanics tracking at 60 FPS
• Serve speed analysis (225 km/h capability)
• Ground force measurement (1900 N torque)
• Pronation/supination tracking

Monitor facility systems:

• Smart HVAC climate control
• Robotic maintenance status
• Court surface quality metrics
• Energy consumption analytics

Built on OpenTwins technology:

• Eclipse Ditto: Digital twin definitions
• Eclipse Hono: IoT device integration
• Real-time monitoring and control
• Predictive maintenance algorithms

Inspired by Voyager and Eureka methodologies:

• Automatic Curriculum: Dynamic task progression
• Skill Library: Reusable action patterns
• Reward Optimization: Evolutionary performance tuning
• Self-Verification: Continuous improvement loops

• Environment feedback integration
• Execution error analysis
• Iterative skill refinement
• Performance metrics tracking

1. Autonomous Excellence: Achieve 24/7 operation with minimal human intervention
2. Sustainability: Net-zero energy consumption through solar and smart systems
3. User Experience: Seamless booking, access, and service delivery
4. Performance: Industry-leading analytics and injury prevention
5. Community: Local partnerships and educational collaborations

• Local sports organizations for cost sharing
• Educational institutions for research collaboration
• Government agencies for sustainability grants

Currently raising Series A funding for Austin, Texas pilot facility. Contact via the Invest page for more information.

• Multi-sensor array for court conditions
• Environmental monitoring (temperature, humidity, air quality)
• Player movement tracking and analysis
• Real-time video analytics

• Energy usage forecasting
• Maintenance schedule optimization
• Player traffic pattern analysis
• Resource allocation modeling

• Centralized control via BMS
• Real-time IoT device communication
• Cloud-based analytics platform
• Mobile app for member access

ace/
├── .github/
│   ├── workflows/        # CI/CD pipelines
│   └── DEPLOYMENT.md     # Deployment documentation
├── components/           # React components
│   ├── NavBar.tsx
│   ├── ThreeScene.tsx
│   ├── AIChat.tsx
│   └── Specifications.tsx
├── services/            # Service layer
├── App.tsx              # Main application
├── index.tsx            # Entry point
├── types.ts             # TypeScript definitions
└── vite.config.ts       # Build configuration

The project supports dynamic base paths for multi-environment deployment:

// vite.config.ts
const base = process.env.VITE_BASE_PATH || '/';

Set via environment variable during build:

VITE_BASE_PATH=/dev/ npm run build

• Deployment Guide - GitHub Pages setup and troubleshooting
• AI Studio Link - Original project workspace

This project is licensed under the terms specified in LICENSE.

• OpenTwins for digital twin infrastructure
• Eclipse Foundation for Ditto and Hono frameworks
• Google for Gemini AI integration
• Community Contributors for feedback and support