Communication-Aware Asynchronous Distributed Trajectory Optimization for UAV Swarm

TL;DR

This paper introduces a communication-aware asynchronous distributed framework for UAV swarm trajectory optimization that reduces communication needs and handles unreliable links effectively.

Contribution

It presents a novel two-tier architecture combining PDDP and async-ADMM for efficient, communication-constrained distributed UAV trajectory planning.

Findings

Reduces communication overhead significantly.

Handles nonlinear dynamics and spatio-temporal coupling.

Effective in unreliable communication environments.

Abstract

Distributed optimization offers a promising paradigm for trajectory planning in Unmanned Aerial Vehicle (UAV) swarms, yet its deployment in communication-constrained environments remains challenging due to unreliable links and limited data exchange. This paper addresses this issue via a two-tier architecture explicitly designed for operation under communication constraints. We develop a Communication-Aware Asynchronous Distributed Trajectory Optimization (CA-ADTO) framework that integrates Parameterized Differential Dynamic Programming (PDDP) for local trajectory optimization of individual UAVs with an asynchronous Alternating Direction Method of Multipliers (async-ADMM) for swarm-level coordination. The proposed architecture enables fully distributed optimization while substantially reducing communication overhead, making it suitable for real-world scenarios in which reliable connectivity cannot be guaranteed. The method is particularly effective in handling nonlinear dynamics and spatio-temporal coupling under communication constraints.