Code & Development · GitHub ·13 ★

D2OC

Python and MATLAB codes for Density-Driven Optimal Control (D2OC) using Optimal Transport and Wasserstein distance, enabling decentralized multi-agent / multi-robot non-uniform area coverage.

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Details

Author
kooktaelee
Category
Code & Development
Platform
GitHub
Framework
custom
Language
matlab
Stars
13
First indexed
2026-05-15
Last active
2026-03-03
Directory sync
2026-05-15
@kooktaelee

Overview

Python and MATLAB codes for Density-Driven Optimal Control (D2OC) using Optimal Transport and Wasserstein distance, enabling decentralized multi-agent / multi-robot non-uniform area coverage.

Quick start

git

git clone https://github.com/kooktaelee/D2OC

Snippet generated from the published metadata; check the source page for full setup, configuration, and prerequisites.

What D2OC can do

ragcodemonitor
  • Rag — Retrieves grounded context before answering.
  • Code — Reads and modifies code in your repository.
  • Monitor — monitor task automation.

Frequently asked questions

What is D2OC?
Python and MATLAB codes for Density-Driven Optimal Control (D2OC) using Optimal Transport and Wasserstein distance, enabling decentralized multi-agent / multi-robot non-uniform area coverage.
How do I install D2OC?
Use git: `git clone https://github.com/kooktaelee/D2OC`. Full setup details on the source page linked above.
Is D2OC open source?
D2OC is published on GitHub.
What are alternatives to D2OC?
Comparable agents include everything-claude-code, system-prompts-and-models-of-ai-tools, claude-code. Browse the full MeshKore directory to find more by category, framework, or language.

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Source & freshness

Profile data for D2OC is sourced from GitHub, published by kooktaelee.

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