We consider a setting in which one swarm of agents is to service or track a second swarm, and formulate an optimal control problem which trades off between the competing objectives of servicing and motion costs. We consider the continuum limit where large-scale swarms are modeled in terms of their time-varying densities, and where the Wasserstein distance between two densities captures the servicing cost. We show how this non-linear infinite-dimensional optimal control problem is intimately related to the geometry of Wasserstein space, and provide new results in the case of absolutely continuous densities and constant-in-time references. Specifically, we show that optimal swarm trajectories follow Wasserstein geodesics, while the optimal control tradeoff determines the time-schedule of travel along these geodesics. We briefly describe how this solution provides a basis for a model-predictive control scheme for tracking time-varying and real-time reference trajectories as well.
翻译:我们考虑一个问题:一个群体要服务或追踪另一个群体,并制定一个最优控制问题来权衡服务和运动成本之间的竞争目标。我们考虑大规模群体的连续极限,其中人口密度随时间变化,并且Wasserstein距离表示服务成本。我们展示这个非线性无限维最优控制问题与Wasserstein空间的几何密切相关,并针对绝对连续密度和时间常数参考提供新的结果。具体而言,我们展示了最优群体轨迹遵循Wasserstein测地线,而最优控制权衡确定了沿着这些测地线的旅行时间表。我们简要描述了如何使用这个解决方案为追踪时间变化和即时参考轨迹提供模型预测控制方案的基础。