SkyGrid: Energy-Flow Optimization at Harmonized Aerial Intersections

TL;DR

This paper introduces SkyGrid, a novel multi-objective optimization framework for managing energy, flow, and safety at aerial intersections in urban air mobility, adapting rhythmic control for autonomous aerial vehicles.

Contribution

It is the first to apply multi-objective optimization for unsignalized aerial intersections within a rhythmic control framework, addressing energy, throughput, and safety simultaneously.

Findings

Effective traffic management under various demand scenarios.

Significant reduction in routing computation complexity.

Demonstrated safety and efficiency improvements.

Abstract

The rapid evolution of urban air mobility (UAM) is reshaping the future of transportation by integrating aerial vehicles into urban transit systems. The design of aerial intersections plays a critical role in the phased development of UAM systems to ensure safe and efficient operations in air corridors. This work adapts the concept of rhythmic control of connected and automated vehicles (CAVs) at unsignalized intersections to address complex traffic control problems. This control framework assigns UAM vehicles to different movement groups and significantly reduces the computation of routing strategies to avoid conflicts. In contrast to ground traffic, the objective is to balance three measures: minimizing energy utilization, maximizing intersection flow (throughput), and maintaining safety distances. This optimization method dynamically directs traffic with various demands, considering path assignment distributions and segment-level trajectory coefficients for straight and curved paths as control variables. To the best of our knowledge, this is the first work to consider a multi-objective optimization approach for unsignalized intersection control in the air and to propose such optimization in a rhythmic control setting with time arrival and UAM operational constraints. A sensitivity analysis with respect to inter-platoon safety and straight/left demand balance demonstrates the effectiveness of our method in handling traffic under various scenarios.