DamianoBrunori/DAMIAN-Delay-Aware-MultI-Aerial-Navigation-DRL-based-environment-

An OpenAIGym-based framework allowing to test Delay-Aware Deep Reinforcement Learning algorithms for cooperative multi-UAV systems in fully customizable scenarios (e.g., coverage maximization, tracking, spotting).

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Emerging

This project helps operations engineers and mission planners design and evaluate strategies for coordinating multiple Unmanned Aerial Vehicles (UAVs) to achieve specific goals, such as maximizing area coverage or tracking a moving target. You can input various scenario parameters like the number of UAVs, their flight capabilities, and environmental conditions, and it outputs simulated mission performance and optimal flight paths. This is for professionals managing drone fleets who need to test different operational strategies and account for real-world delays.

No commits in the last 6 months.

Use this if you need to simulate and optimize multi-drone missions, especially when considering the impact of communication or action delays in dynamic environments.

Not ideal if you're looking for a simple, out-of-the-box flight planning tool for a single drone or basic mapping tasks without complex optimization.

drone-operations mission-planning aerial-surveillance UAV-fleet-management operations-research
Stale 6m No Package No Dependents
Maintenance 2 / 25
Adoption 8 / 25
Maturity 16 / 25
Community 14 / 25

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Stars

57

Forks

8

Language

Python

License

MIT

Last pushed

Sep 25, 2025

Commits (30d)

0

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