Communication-Aware Asynchronous Distributed Trajectory Optimization for UAV Swarm

2025-11-19

AI Summary

To address the challenges of poor convergence and low coordination efficiency in distributed trajectory optimization for UAV swarms under communication constraints, this paper proposes a two-layer asynchronous collaborative optimization framework. At the upper layer, asynchronous alternating direction method of multipliers (async-ADMM) enables decentralized, low-frequency swarm coordination without a central node. At the lower layer, parametrized differential dynamic programming (PDDP) performs local nonlinear trajectory optimization, augmented with a lightweight communication-aware mechanism to accommodate unreliable links. The framework rigorously respects spatiotemporal coupling constraints and dynamical fidelity while significantly reducing communication overhead, enhancing robustness, and improving scalability. Experimental results demonstrate efficient and stable convergence even under low signal-to-noise ratio and high packet-loss conditions, validating its suitability for large-scale, dynamically reconfigurable UAV swarm deployments.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Develops distributed trajectory optimization for UAV swarms under communication constraints
• Integrates local trajectory planning with asynchronous swarm-level coordination
• Addresses nonlinear dynamics and spatio-temporal coupling with reduced communication overhead

Innovation

• Communication-aware asynchronous distributed optimization framework
• Integrates PDDP for local trajectory optimization
• Uses async-ADMM for swarm-level coordination