  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.
