Barsi Haberfeld, Gabriel (University of Illinois at Urbana-Champaign), Gahlawat, Aditya (University of Illinois at Urbana-Champaign), Hovakimyan, Naira (University of Illinois at Urbana-Champaign)

Safe Sampling-Based Air-Ground Rendezvous Algorithm for Complex Dense Street Maps

Scheduled for presentation during the Regular Session "FDI and Safety" (ThA1), Thursday, June 17, 2021, 11:50−12:10, Macedonia Hall

2021 International Conference on Unmanned Aircraft Systems (ICUAS), June 15-18, 2021, Athens, Greece

This information is tentative and subject to change. Compiled on April 26, 2024

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 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.