AeroPlanX

Inspiration

Mission planning in aerospace is still too slow, too manual and too dependent on heavyweight tools and libraries. Whether it’s UAV route planning or basic orbit analysis, engineers often face a tradeoff between accuracy and speed.

AeroPlanX was inspired by a simple question: What if mission planning could be automated, lightweight and fast, without sacrificing physical realism?

We wanted a system that could generate good-enough, physically grounded plans in real time, using AI-assisted logic and simplified but credible aerospace models.

What it does

AeroPlanX is an AI-powered mission planning and trajectory optimization system for aircraft and spacecraft. It: Generates feasible flight and orbital trajectories Accounts for real-world constraints (energy, maneuverability, safety) Simulates environmental effects like wind and turbulence Runs fast Monte-Carlo simulations to test robustness Works without heavy external aerospace libraries The result is a self-contained planning engine that balances realism, speed, and explainability.

How I built it

AeroPlanX was built using a modular, physics-aware architecture: Orbit Propagation Closed-form two-body dynamics with J2 perturbation No external orbit libraries (e.g., SGP4) to keep the system transparent and portable

Aircraft Solver Greedy path planner with constraint-repair logic Optimized for real-time UAV planning rather than exhaustive search Supports Monte-Carlo evaluation in under 10 seconds

Environmental Modeling Dryden turbulence model for realistic wind disturbances Spatial sinusoidal base wind field for variability

System Design Python-based core Clean separation between solvers, models and simulation logic Designed for rapid experimentation and hackathon deployment

Challenges I ran into

Balancing physical accuracy vs computational speed Designing solvers that are fast enough for real-time use but still credible Avoiding dependency on heavyweight aerospace libraries while keeping results meaningful Ensuring Monte-Carlo simulations remained stable under turbulence Each challenge forced deliberate tradeoffs, and better engineering decisions.

Accomplishments that I'm proud of

Built a fully self-contained aerospace planning system Achieved fast Monte-Carlo robustness testing (<10s) Implemented realistic turbulence modeling without external tools Designed a system that’s both explainable and extensible Delivered a project that feels like a research prototype, not a demo

What I learned

Simpler models, when used correctly, can outperform complex ones in real-time systems Constraint repair can be more practical than full optimal search in aerospace applications Explainability matters as much as optimization in mission planning Good structure and documentation dramatically improve project credibility

What's next for AeroPlanX

Multi-agent mission planning (swarms & formations) Hybrid AI + optimization solvers Visualization dashboard for trajectories and uncertainty Support for real-time sensor feedback Expansion into disaster response and autonomous logistics planning

Built With

• css3
• html5
• javascript
• mysql
• php
• python
• sql
• vanilla