让多智能体在固定路线上安全高效地调度速度，避免碰撞且不改变路径顺序。
Collision-Free Velocity Scheduling for Multi-Agent Systems on Predefined Routes via Inexact-Projection ADMM
- 用时间调度替代路径重规划，通过平滑轨迹模型建模位置与时间关系。
- 在瓶颈场景下任务完成时间比基线方法更短，且在不同拥堵程度下均有效。
- 无需整数变量，结合梯度修正和结构化更新，适合复杂交通网络部署。
在结构化的多智能体运输系统中，智能体通常需遵循预设路线，使得空间路径重规划不可行或不被允许。本文通过优化各智能体通过航点的时间，在保持其指定航点顺序和原始路线分配的前提下，实现路线约束下的多智能体协同。采用可微分的近似轨迹模型将航点时间映射为平滑的位置轨迹，并捕捉一阶跟踪延迟，通过在任务周期内密集时间网格上评估基于距离的惩罚项来编码成对安全性。由此产生的非线性、非凸速度调度问题，通过一种非精确投影交替方向乘子法（inexact-projection ADMM）求解，该算法结合结构化时间更新与基于梯度的碰撞修正步骤，避免了显式的整数序列变量。在随机交叉、瓶颈及图状网络场景下的数值实验表明，所提方法在多种拥堵水平下均可生成可行且时间高效的调度方案，在测试的瓶颈案例中，任务完成时间优于代表性分层基线方法。
原文摘要 · Abstract (English)
In structured multi-agent transportation systems, agents often must follow predefined routes, making spatial rerouting undesirable or impossible. This paper addresses route-constrained multi-agent coordination by optimizing waypoint passage times while preserving each agent's assigned waypoint order and nominal route assignment. A differentiable surrogate trajectory model maps waypoint timings to smooth position profiles and captures first-order tracking lag, enabling pairwise safety to be encoded through distance-based penalties evaluated on a dense temporal grid spanning the mission horizon. The resulting nonlinear and nonconvex velocity-scheduling problem is solved using an inexact-projection Alternating Direction Method of Multipliers (ADMM) algorithm that combines structured timing updates with gradient-based collision-correction steps and avoids explicit integer sequencing variables. Numerical experiments on random-crossing, bottleneck, and graph-based network scenarios show that the proposed method computes feasible and time-efficient schedules across a range of congestion levels and yields shorter mission completion times than a representative hierarchical baseline in the tested bottleneck cases.
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