Communication-Aware Asynchronous Distributed Trajectory Optimization for UAV Swarm

TL;DR

This work tackles trajectory optimization for UAV swarms under unreliable communication by introducing CA-ADTO, a two-tier framework that couples local PDDP-based trajectory optimization with a swarm-wide async-ADMM coordinator. The method explicitly models communication losses through a probabilistic link model and uses safe-copy variables and consensus mechanisms to achieve spatio-temporal coordination without requiring synchronous updates. Key contributions include a fully distributed optimization scheme that (i) handles nonlinear dynamics and collision/communication constraints, (ii) supports flexible terminal-time coordination (sequential, simultaneous, or time-bounded), and (iii) demonstrates robustness and scalability under varying connection probabilities. The simulations show that CA-ADTO can closely match ideal communication scenarios at moderate connectivity and maintain safe, coordinated trajectories in the presence of link unreliability, highlighting its potential for real-world UAV swarm deployments in communication-constrained 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.

Figures (8)

• Figure 1: Schematic diagram of a two-tiered architecture for CA-ADTO.
• Figure 2: An example showing the operation of the partial barrier and bounded delay.
• Figure 3: Distributed spatial-temporal joint optimization results ($p_{con}=\mathrm{70\%}$).
• Figure 4: Distributed spatial-temporal joint optimization results ($p_{con}=\mathrm{50\%}$).
• Figure 5: Distributed spatial-temporal joint optimization results ($p_{con}=\mathrm{30\%}$).

Theorems & Definitions (2)

• Definition 1: Neighbor set
• Definition 2: Deemed Neighbor Set