📦 Release v2.0.0: Python Implementation & Cross-Platform Support
Tag version: v2.0.0 Release title: Python Implementation & Cross-Platform Support (D2OC)
Description
Following the initial MATLAB release, this version introduces the official Python implementation of the Density-Driven Optimal Control (D2OC) framework. This update enables researchers and engineers to integrate D2OC into modern AI and robotics pipelines more easily.
What's New in Python Implementation:
Core Logic: Full port of the Optimal Transport-based decentralized control.
Ease of Use: Single-file execution (D2OC_main.py) for simplified research integration.
Environment: Compatible with standard scientific Python stacks (NumPy, Matplotlib).
Cross-Platform: Ready for Linux, macOS, and Windows without proprietary software requirements.
Key Features (D2OC Framework):
Optimal Transport Control using Wasserstein distance.
Decentralized Weight Update logic for multi-agent coordination.
Quadrotor-inspired LTI Dynamics (8-state formulation).
Non-uniform Density Coverage for complex environment mapping.
Official Links:
📄 Paper (IEEE TSMC): https://doi.org/10.1109/TSMC.2025.3622075
📚 arXiv: https://arxiv.org/abs/2511.12756
🛸 Live Demo (Hugging Face): D2OC Space