Trajectory Design for UAV-Assisted Logistics Collection in Low-Altitude Economy

TL;DR

This paper introduces a novel UAV trajectory planning algorithm combining LKH and DDPG methods to optimize logistics collection in low-altitude economy environments, significantly reducing collection time.

Contribution

It presents a new hybrid algorithm that integrates combinatorial optimization and deep reinforcement learning for UAV trajectory design in obstacle-rich environments.

Findings

Achieves approximately 49% reduction in collection time.

Effectively optimizes UAV trajectories in complex 3D environments.

Demonstrates improved operational efficiency over baseline methods.

Abstract

Low-altitude economy (LAE) is rapidly emerging as a key driver of innovation, encompassing economic activities taking place in airspace below 500 meters. Unmanned aerial vehicles (UAVs) provide valuable tools for logistics collection within LAE systems, offering the ability to navigate through complex environments, avoid obstacles, and improve operational efficiency. However, logistics collection tasks involve UAVs flying through complex three-dimensional (3D) environments while avoiding obstacles, where traditional UAV trajectory design methods,typically developed under free-space conditions without explicitly accounting for obstacles, are not applicable. This paper presents, we propose a novel algorithm that combines the Lin-Kernighan-Helsgaun (LKH) and Deep Deterministic Policy Gradient (DDPG) methods to minimize the total collection time. Specifically, the LKH algorithm determines the optimal order of item collection, while the DDPG algorithm designs the flight trajectory between collection points. Simulations demonstrate that the proposed LKH-DDPG algorithm significantly reduces collection time by approximately 49 percent compared to baseline approaches, thereby highlighting its effectiveness in optimizing UAV trajectories and enhancing operational efficiency for logistics collection tasks in the LAE paradigm.