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.

The generation of route descriptions is a fundamental task of navigation systems. A particular problem in this context is to identify routes that can easily be described and processed by users. In this work, we present a framework for representing route networks with the qualitative information necessary to evaluate and optimize route descriptions with regard to ambiguities in them. We identify different agent models that differ in how agents are assumed to process route descriptions while navigating through route networks. Further, we analyze the computational complexity of matching route descriptions and paths in route networks in dependency of the agent model. Finally we empirically evaluate the influence of the agent model on the optimization and the processing of route instructions.