发表机构
Texas Tech University（德克萨斯理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出非线性密度驱动最优控制（D2OC）扩展，通过序列凸规划实现多智能体空间覆盖，在保持分布式密度驱动特性的同时，显著降低计算成本并保证覆盖性能。
AI 中文摘要
本文提出了密度驱动最优控制（D2OC）的非线性扩展，用于具有指定密度分布的多智能体空间覆盖问题。该方法不分配个体目标位置，而是通过基于Wasserstein距离的目标函数，驱动智能体的集体空间分布趋向期望密度。我们将该框架扩展到离散时间控制仿射非线性系统的多步有限时域控制，采用序列凸规划方法。在每次控制更新时，非线性动力学在预测时域内进行局部线性化，产生一个严格凸的二次规划问题，该问题保留了Wasserstein重心结构，同时直接纳入输入约束。我们进一步刻画了约束控制偏差和非线性泰勒余项对局部线性预测精度的影响，建立了显式的有限时域误差界，并给出了用于滚动时域实现的两步特化。所提方法保留了D2OC的分布式、密度驱动特性，同时为非线性多智能体系统提供了计算高效的优化过程。使用独轮车和四旋翼团队的仿真表明，覆盖性能与非线性模型预测控制相当，同时大幅减少了计算时间。这些结果展示了在非线性动力学下进行密度驱动空间覆盖的一种可处理且具有理论表征的框架。
英文摘要
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.
