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• W3137738847 abstract "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." @default.
• W3137738847 created "2021-03-29" @default.
• W3137738847 creator A5029998748 @default.
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• W3137738847 date "2021-03-12" @default.
• W3137738847 modified "2023-09-27" @default.
• W3137738847 title "Safe Sampling-Based Air-Ground Rendezvous Algorithm for Complex Urban Environments." @default.
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• W3137738847 hasPublicationYear "2021" @default.
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