Density-Driven Optimal Control (D²OC) for multi-agent systems - optimal transport-based coverage control with Wasserstein distance minimization. Use when: (1) multi-agent coverage problems, (2) optimal transport in control systems, (3) Wasserstein distance applications, (4) stochastic MPC for swarms, (5) decentralized area coverage, (6) density matching problems.
A rigorous Lagrangian framework for non-uniform area coverage in stochastic multi-agent systems using optimal transport theory.
D²OC reformulates multi-agent coverage as an optimal transport problem, minimizing Wasserstein distance between agent distribution and target density in a stochastic MPC-like formulation.
Key Innovation: Bridges individual agent dynamics with collective distribution matching via optimal transport, with formal convergence guarantees under noise.
Each agent follows:
x_{k+1}^i = A_i x_k^i + B_i u_k^i + w_k^i, w_k^i ~ N(0, Σ_i,w)
y_k^i = C_i x_k^i + v_k^i, v_k^i ~ N(0, Σ_i,v)