Inspiration
What it does
How we built it## Inspiration
Drone delivery and other UAV applications are growing, but coordinating multiple drones while managing limited battery capacity remains a complex challenge. IndoAirTraffic AI was inspired by the need for smarter, safer, and more energy-efficient drone operations.
What it does
IndoAirTraffic AI is a drone air traffic management prototype that combines AI-assisted mission planning with battery-aware route optimization. It is designed to help users visualize drone routes, identify potential flight-path conflicts, estimate battery requirements, and explore alternative routes when a mission encounters a constraint.
Key Features
- AI-assisted flight planning and mission explanations.
- Battery-aware route recommendations.
- Multi-drone route conflict detection.
- Low-battery alerts and alternative landing recommendations.
- Interactive airspace visualization and simulated drone telemetry.
How we built it
The planned architecture combines a web dashboard, a mapping interface, route optimization algorithms, and a generative AI assistant. The system can be developed using JavaScript, Node.js, a mapping library, and an AI API. Drone movements and battery readings can be simulated to demonstrate the workflow without requiring physical drone hardware.
Challenges
The main challenges include balancing route distance with battery consumption, identifying conflicts between drone paths, and ensuring that AI-generated recommendations are checked against explicit routing constraints.
What's next
Future improvements include integrating real drone telemetry, weather-aware energy estimation, charging-station planning, and PX4/Gazebo simulation for more realistic testing.
Responsible design
This prototype is intended for research and simulation. Its route suggestions are not a substitute for certified air traffic management, regulatory approval, or validated flight-safety systems.
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