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Kooktae Lee edited this page Feb 22, 2026
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Welcome to the official repository for Density-Driven Optimal Control (D2OC). This project provides the implementation of the research published in IEEE Transactions on Systems, Man, and Cybernetics: Systems.
Unlike traditional coverage control, which focuses on reaching a static final robot distribution, D2OC introduces a dynamic, density-driven paradigm closer to finite-time ergodic control.
Most conventional coverage methods (e.g., CVT) are designed for stationary robot placement. D2OC redefines multi-agent coordination by treating the coverage problem as a dynamic process of matching time-averaged agent statistics to spatial density maps.
- Dynamic Density Tracking: Moves beyond static placement to achieve continuous, ergodic-like coverage of nonuniform areas.
- Optimal Transport Framework: Utilizes the L2 Wasserstein distance to mathematically guarantee that agent mass "flows" to represent the target density map over time.
- Fully Decentralized Architecture: Computational cost remains scalable as agent numbers increase, with each agent using only local weight updates.
- High-Fidelity Dynamics: Supports Quadrotor-inspired LTI dynamics (8-state models), ensuring the control logic is applicable to real-world robotic systems.
Experience the difference between static coverage and D2OC's dynamic tracking.
- Interactive Demo: Watch how agents dynamically explore the density map.
- MATLAB Implementation: The original research code for IEEE TSMC publication.
- Python Implementation: Cross-platform version for modern robotics research.
@ARTICLE{seo2025density,
author={Seo, Sungjun and Lee, Kooktae},
journal={IEEE Transactions on Systems, Man, and Cybernetics: Systems},
title={Density-Driven Optimal Control for Efficient and Collaborative Multiagent Nonuniform Coverage},
year={2025},
volume={55},
number={12},
pages={9340-9354},
doi={10.1109/TSMC.2025.3622075}}