Demand for fast and economical parcel deliveries in urban environments has risen considerably in recent years. A framework envisions efficient last-mile delivery in urban environments by leveraging a network of ride-sharing vehicles, where Unmanned Aerial Systems (UASs) drop packages on said vehicles, which then cover the majority of the distance before final aerial delivery. Notably, we consider the problem of planning a rendezvous path for the UAS to reach a human driver, who may choose between N possible paths and has uncertain behavior, while meeting strict safety constraints. The long planning horizon and safety constraints require robust heuristics that combine learning and optimal control using Gaussian Process Regression, sampling-based optimization, and Model Predictive Control. The resulting algorithm is computationally efficient and shown to be effective in a variety of qualitative scenarios.

翻译：近年来,在城市环境中快速、经济的包裹交付需求大幅上升,一个框架设想在城市环境中高效的最后一英里交付,办法是利用搭载车辆网络,使无人驾驶航空系统(UAS)向上述车辆投放包件,然后覆盖最终空中交付之前的距离。值得注意的是,我们考虑了为无人驾驶系统规划一个会合路径的问题,以便让无人驾驶系统到达一个人类司机,他可以在可能的道路之间作出选择,并有不确定的行为,同时要满足严格的安全限制。长期规划前景和安全限制需要强有力的超时主义,利用高山进程回归、抽样优化和模型预测控制,将学习和最佳控制结合起来。 由此产生的算法在计算上是有效的,在各种定性情景中证明是有效的。