Attention-based UAV Trajectory Optimization for Wireless Power Transfer-assisted IoT Systems

TL;DR

This paper introduces an attention-based framework for optimizing UAV trajectories in wireless power transfer IoT systems, improving efficiency and scalability over traditional reinforcement learning methods.

Contribution

It proposes a novel graph transformer-based model and an actor-critic training method tailored for large-scale multi-UAV trajectory optimization.

Findings

Effective in large-scale scenarios

Reduces variance in reinforcement learning

Validated through hardware experiments

Abstract

Unmanned Aerial Vehicles (UAVs) in Wireless Power Transfer (WPT)-assisted Internet of Things (IoT) systems face the following challenges: limited resources and suboptimal trajectory planning. Reinforcement learning-based trajectory planning schemes face issues of low search efficiency and learning instability when optimizing large-scale systems. To address these issues, we present an Attention-based UAV Trajectory Optimization (AUTO) framework based on the graph transformer, which consists of an Attention Trajectory Optimization Model (ATOM) and a Trajectory lEarNing Method based on Actor-critic (TENMA). In ATOM, a graph encoder is used to calculate the self-attention characteristics of all IoTDs, and a trajectory decoder is developed to optimize the number and trajectories of UAVs. TENMA then trains the ATOM using an improved Actor-Critic method, in which the real reward of the system is applied as the baseline to reduce variances in the critic network. This method is suitable for high-quality and large-scale multi-UAV trajectory planning. Finally, we develop numerous experiments, including a hardware experiment in the field case, to verify the feasibility and efficiency of the AUTO framework.