Nonlinear control method improves multi-agent area coverage efficiency
Nonlinear Density-Driven Optimal Control (D2OC) for Multi-Agent Spatial Coverage via Sequential Convex Programming
Multiagent SystemsRobotics
Summary
Controlling many robots to cover an area evenly is challenging, especially when their movements follow complex rules. The authors develop a new method that treats the group like a fluid with a target density shape rather than assigning each robot a specific spot. They use a step-by-step approach that simplifies the complex dynamics at each step, making it computationally faster while keeping good coverage quality. Tests show their method works well for different robot types, performing similarly to advanced control methods but with less computing effort.
What this means in practice
- •For robotics teams: Control robot teams to cover areas with target densities more efficiently under nonlinear movement constraints using faster optimization.
- •For drone operators: Plan drone swarm movement to cover spatial regions evenly while respecting nonlinear flight dynamics and input limits.
Authors
Julian Martinez, Kooktae Lee
Abstract
This paper presents a nonlinear extension of Density-Driven Optimal Control (D2OC) for multi-agent spatial coverage with prescribed density distributions. Rather than assigning individual target locations, D2OC drives the collective spatial distribution of agents toward a desired density through a Wasserstein-based objective. We extend this framework to multi-step finite-horizon control for discrete-time control-affine nonlinear systems using sequential convex programming. At each control update, the nonlinear dynamics are locally linearized over the prediction horizon, yielding a strictly convex quadratic program that preserves the Wasserstein barycentric structure while directly incorporating input constraints. We further characterize the effect of constrained control deviations and nonlinear Taylor remainders on the accuracy of the local linear prediction, establishing an explicit finite-horizon error bound and a two-step specialization for receding-horizon implementation. The resulting method retains the decentralized, distribution-driven nature of D2OC while providing a computationally efficient optimization procedure for nonlinear multi-agent systems. Simulations with unicycle and quadrotor teams show coverage performance comparable to nonlinear model predictive control, while substantially reducing computation time. These results demonstrate a tractable and theoretically characterized framework for density-driven spatial coverage under nonlinear dynamics.