7 papers
Nonlinear Density-Driven Optimal Control (D2OC) for Multi-Agent Spatial Coverage via Sequential Convex Programming
Julian Martinez, Kooktae Lee
This paper presents a nonlinear extension of Density-Driven Optimal Control (D2OC) for multi-agent spatial coverage with prescribed density distributions. Rather than assigning ind…
Density-Driven Area Coverage for Nonholonomic Multi-Robot Systems with Safety Guarantee
Julian Martinez, Kooktae Lee
Density-Driven Optimal Control (D2OC) provides a principled approach to distributing multi-robot teams over non-uniform spatial distributions. Applying D2OC to nonholonomic robots,…
Density-Driven Optimal Control: Convergence Guarantees for Stochastic LTI Multi-Agent Systems
Kooktae Lee
This paper addresses the decentralized non-uniform area coverage problem for multi-agent systems, a critical task in missions with high spatial priority and resource constraints. W…
Feedforward Density-Driven Optimal Control for Tracking Time-Varying Distributions with Guaranteed Stability
Julian Martinez, Kooktae Lee
This paper addresses the spatiotemporal mismatch in multi-agent distribution tracking within time-varying environments. While recent advancements in Density-Driven Optimal Control…
Computationally Efficient Density-Driven Optimal Control via Analytical KKT Reduction and Contractive MPC
Julian Martinez, Kooktae Lee
Efficient coordination for collective spatial distribution is a fundamental challenge in multi-agent systems. Prior research on Density-Driven Optimal Control (D2OC) established a…
AI-Augmented Density-Driven Optimal Control (D2OC) for Decentralized Environmental Mapping
Kooktae Lee, Julian Martinez
This paper presents an AI-augmented decentralized framework for multi-agent (multi-robot) environmental mapping under limited sensing and communication. While conventional coverage…
Breaking Symmetry-Induced Degeneracy in Multi-Agent Ergodic Coverage via Stochastic Spectral Control
Kooktae Lee, Julian Martinez
Multi-agent ergodic coverage via Spectral Multiscale Coverage (SMC) provides a principled framework for driving a team of agents so that their collective time-averaged trajectories…