A Route Network Planning Method for Urban Air Delivery

TL;DR

This paper presents a novel route network planning method for urban drone delivery, enabling efficient, dense, and aligned route design in complex environments, crucial for scalable urban air mobility.

Contribution

It introduces a decoupling approach for NP-hard network planning and a new space cost function to improve route density and alignment.

Findings

Generates near-optimal route networks with reduced computational time

Outperforms existing methods in dense urban scenarios

Facilitates scalable urban air delivery systems

Abstract

High-tech giants and start-ups are investing in drone technologies to provide urban air delivery service, which is expected to solve the last-mile problem and mitigate road traffic congestion. However, air delivery service will not scale up without proper traffic management for drones in dense urban environment. Currently, a range of Concepts of Operations (ConOps) for unmanned aircraft system traffic management (UTM) are being proposed and evaluated by researchers, operators, and regulators. Among these, the tube-based (or corridor-based) ConOps has emerged in operations in some regions of the world for drone deliveries and is expected to continue serving certain scenarios that with dense and complex airspace and requires centralized control in the future. Towards the tube-based ConOps, we develop a route network planning method to design routes (tubes) in a complex urban environment in this paper. In this method, we propose a priority structure to decouple the network planning problem, which is NP-hard, into single-path planning problems. We also introduce a novel space cost function to enable the design of dense and aligned routes in a network. The proposed method is tested on various scenarios and compared with other state-of-the-art methods. Results show that our method can generate near-optimal route networks with significant computational time-savings.