基于最优传输的多智能体非均匀覆盖控制，提升高优先区覆盖率
On the Convergence of Density-Based Predictive Control for Multi-Agent Non-Uniform Area Coverage
- 用参考分布指导智能体分配覆盖资源，优先覆盖高价值区域
- 仿真显示轨迹逼近目标分布，优于传统覆盖方法
- 适合搜救、环境监测等需差异化覆盖的任务场景
本文提出密度基预测控制（DPC），一种基于最优传输理论的多智能体非均匀区域覆盖新策略。在搜救或环境监测等大规模场景中，传统均匀覆盖无法反映区域优先级差异。DPC利用预构建的参考分布，引导智能体在高优先级或已密集采样区域投入更多覆盖时间。通过Wasserstein距离分析收敛性，推导出无约束情况下的解析最优控制律，并为有约束情形提出数值求解方法。在一阶动力学和线性化四旋翼模型上的仿真表明，DPC能生成高度匹配非均匀参考分布的轨迹，显著优于现有覆盖方法。
原文摘要 · Abstract (English)
This paper presents Density-based Predictive Control (DPC), a novel multi-agent control strategy for efficient non-uniform area coverage, grounded in optimal transport theory. In large-scale scenarios such as search and rescue or environmental monitoring, traditional uniform coverage fails to account for varying regional priorities. DPC leverages a pre-constructed reference distribution to allocate agents' coverage efforts, spending more time in high-priority or densely sampled regions. We analyze convergence conditions using the Wasserstein distance, derive an analytic optimal control law for unconstrained cases, and propose a numerical method for constrained scenarios. Simulations on first-order dynamics and linearized quadrotor models demonstrate that DPC achieves trajectories closely matching the non-uniform reference distribution, outperforming existing coverage methods.
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