Reinforcement Learning for Autonomous Satellite Constellation Scheduling and Earth Observation Optimization
SatelliteRL addresses the complex challenge of optimizing satellite constellation operations for Earth observation missions. Using advanced reinforcement learning techniques, this system learns to schedule imaging requests, manage power resources, and coordinate multiple satellites to maximize scientific and commercial value.
Key Innovation: Multi-agent RL approach that treats each satellite as an autonomous agent while maintaining constellation-level coordination through shared objectives and communication protocols.
Modern Earth observation requires coordinating dozens of satellites with:
- Competing Priorities: Emergency response vs. routine monitoring vs. commercial requests
- Resource Constraints: Limited power, storage, and communication windows
- Dynamic Environment: Weather conditions, orbital mechanics, equipment failures
- Multi-Objective Optimization: Scientific value, cost efficiency, customer satisfaction
- Basic orbital simulation environment
- Satellite dynamics modeling
- Simple reward function framework
- Ground station visibility calculations
- Weather integration (cloud cover)
- Multi-agent DQN implementation
- Experience replay optimization
- Real TLE data integration
- Advanced reward shaping
- Hierarchical RL for complex action spaces
- Real-time visualization dashboard
- Industry benchmark comparisons
- Multi-satellite coordination protocols
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Environment │ │ RL Agents │ │ Coordinator │
│ │ │ │ │ │
│ Orbital Sim │◄──►│ Satellite 1 │◄──►│ Task Scheduler │
│ Weather Data │ │ Satellite 2 │ │ Resource Mgmt │
│ Ground Stations│ │ ... │ │ Comm Protocol │
└─────────────────┘ └─────────────────┘ └─────────────────┘
| Metric | Target | Current Status |
|---|---|---|
| Coverage Efficiency | >85% | Baseline: 65% |
| Power Utilization | <5% emergency events | In development |
| Response Time | <2 hours for urgent requests | Testing phase |
| Revenue Optimization | 15-20% improvement | Pending evaluation |
- Core RL: PyTorch, Stable-Baselines3, OpenAI Gym
- Orbital Mechanics: Skyfield, Poliastro, SGP4
- Data Processing: NumPy, Pandas, Scikit-learn
- Visualization: Plotly, Dash, Matplotlib
- APIs: OpenWeatherMap, Space-Track.org
- Repository setup and documentation
- Basic simulation environment
- Orbital mechanics integration
- Initial reward function
- DQN agent implementation
- Multi-agent coordination
- Experience replay optimization
- Training pipeline
- Real-world data integration
- Hierarchical action spaces
- Performance optimization
- Robustness testing
- Interactive dashboard
- Industry benchmarking
- Documentation and presentation
- Open-source release
# Clone repository
git clone https://github.com/debanjan06/SatelliteRL.git
cd SatelliteRL
# Install dependencies
pip install -r requirements.txt
# Run basic simulation
python src/simulation/run_basic_sim.py
# Start training (coming soon)
python src/training/train_dqn.py --config configs/default.yamlThis project addresses real challenges faced by:
- Satellite Operators: Planet Labs, Maxar Technologies, Capella Space
- Space Agencies: NASA, ESA, ISRO, SpaceX
- Commercial Users: Agriculture, disaster response, urban planning
- Research Institutions: Earth observation and climate research
- Novel multi-agent RL formulation for satellite scheduling
- Hierarchical action space design for complex orbital maneuvers
- Real-time adaptation to dynamic weather and operational constraints
- Open-source framework for satellite constellation research
Debanjan Shil
M.Tech Data Science Student
Roll No: BL.SC.P2DSC24032
GitHub: @debanjan06
Interested in collaboration or have questions?
- 📧 Open an issue for technical discussions
- 🔗 Connect on LinkedIn for industry opportunities
- 📝 Check out my other projects on GitHub
Progress: ████████░░░░░░░░░░ 40% Complete
Last Update: June 2025
Next Milestone: Multi-agent DQN implementation
"Optimizing Earth observation through intelligent satellite coordination"