Safe Sampling-Based Air-Ground Rendezvous Algorithm for Complex Urban Environments
This addresses the challenge of reliable last-mile parcel delivery using air-ground coordination, but it is incremental as it builds on existing methods like Gaussian Process Regression and Model Predictive Control.
The paper tackles the problem of planning safe rendezvous paths for unmanned aerial systems (UASs) to meet human-driven vehicles in complex urban environments, with uncertain driver behavior and strict safety constraints, resulting in a computationally efficient algorithm demonstrated as effective in various scenarios.
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.