Safe Sampling-Based Air-Ground Rendezvous Algorithm for Complex Urban Environments

TL;DR

This paper presents a robust, efficient algorithm for planning safe rendezvous paths for UASs in complex urban environments, integrating learning, sampling, and control to handle uncertain driver behavior.

Contribution

It introduces a novel planning algorithm combining Gaussian Process Regression, sampling-based optimization, and Model Predictive Control for urban UAS-ground rendezvous.

Findings

Algorithm is computationally efficient.

Effective in diverse qualitative scenarios.

Handles uncertain driver behavior robustly.

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.