This repository provides the official MATLAB implementation of
Density-Driven Optimal Control (D2OC) — a novel multi-agent control framework based on
Optimal Transport (OT) and Wasserstein distance for
non-uniform area coverage, multi-agent/multi-robot coordination, and
fully decentralized multi-agent control.
This code accompanies the following publication:
📄 Paper (IEEE Transactions on Systems, Man, and Cybernetics: Systems)
👉 DOI: https://doi.org/10.1109/TSMC.2025.3622075
👉 arXiv: https://arxiv.org/abs/2511.12756
D2OC solves decentralized multi-agent area coverage by:
- modeling target maps as probability densities,
- guiding agents using Wasserstein-distance–driven OT potentials,
- applying linearized quadrotor-inspired dynamics,
- computing control inputs via a finite-horizon KKT-based MPC,
- enabling decentralization through local weight sharing.
Included in this repository:
- 8-state quadrotor LTI dynamics
- OT-based target computation
- decentralized weight update logic
- Gaussian-mixture density fields
- simulation data and parameters
- live trajectory visualization
- Optimal Transport control with Wasserstein distance
- Non-uniform density tracking
- Decentralized multi-agent coverage
- Lagrangian-based OT point selection
- Finite-horizon KKT/MPC formulation
- UAV-ready MATLAB implementation
- Scalable to many agents
State vector:
x = [x, x_dot, theta, theta_dot, y, y_dot, phi, phi_dot]'
where:
- x, y = positions
- theta, phi = pitch/roll angles
- derivatives = velocities
Main_D2OC.m → Main simulation script
environment/DF.mat → Reference density maps
param/param07.mat → Control parameters
sim_data/Sim_rev60.mat → Simulation configurations
update_weight_R2.m → Decentralized weight update rule
hamilton_optimal_control... → OT-based target computation
- Clone or download this repository
- Use MATLAB R2020a or later
- Ensure
sim_data/Sim_rev60.matexists - Open
Main_D2OC.m - Select a test:
cnt_sim = 2; % use 2, 3, or 4- Run the script
- Watch live visualization of UAV trajectories and density evolution
- Search & Rescue (SAR)
- Environmental monitoring
- Gas plume / wildfire mapping
- Persistent surveillance
- Agricultural field scanning
- Exploration & inspection
- density-driven optimal control
- optimal transport control
- wasserstein distance
- multi-agent systems
- decentralized control
- distributed UAV control
- coverage control
- non-uniform area coverage
- mpc
- optimal control
- uav robotics
- density control
- multi-robot coordination
If you use this code, please cite:
S. Seo and K. Lee, “Density-Driven Optimal Control for Efficient and Collaborative Multiagent Nonuniform Coverage,”
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2025.
DOI: https://doi.org/10.1109/TSMC.2025.3622075
(arXiv version: https://arxiv.org/abs/2511.12756)
Released under the MIT License.
Maintained by Kooktae Lee, Ph.D.
Associate Professor, Mechanical Engineering, New Mexico Tech