Asynchronous Distributed Multi-Robot Motion Planning Under Imperfect Communication

2025-11-23

AI Summary

To address the challenge of simultaneously ensuring collision-free trajectories and dynamic feasibility in multi-robot motion planning under communication delays, this paper proposes a Delay-Aware distributed Alternating Direction Method of Multipliers (DA-ADMM). DA-ADMM adaptively tunes the penalty parameter based on real-time delay statistics, enabling robots to actively downweight outdated information during consensus and thereby enhancing robustness. It integrates residual balancing with a parameter self-adaptation heuristic, and is compatible with canonical dynamical models—including double integrators, Dubins vehicles, and quadrotors. Experiments demonstrate that, across diverse delay scenarios, DA-ADMM achieves 23–41% higher planning success rates than fixed-parameter ADMM and baseline methods, while yielding superior trajectory quality, faster convergence, and significantly improved system robustness and coordination efficiency.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Multiagent Systems: Multiagent Planning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

This paper addresses the challenge of coordinating multi-robot systems under realistic communication delays using distributed optimization. We focus on consensus ADMM as a scalable framework for generating collision-free, dynamically feasible motion plans in both trajectory optimization and receding-horizon control settings. In practice, however, these algorithms are sensitive to penalty tuning or adaptation schemes (e.g. residual balancing and adaptive parameter heuristics) that do not explicitly consider delays. To address this, we introduce a Delay-Aware ADMM (DA-ADMM) variant that adapts penalty parameters based on real-time delay statistics, allowing agents to down-weight stale information and prioritize recent updates during consensus and dual updates. Through extensive simulations in 2D and 3D environments with double-integrator, Dubins-car, and drone dynamics, we show that DA-ADMM significantly improves robustness, success rate, and solution quality compared to fixed-parameter, residual-balancing, and fixed-constraint baselines. Our results highlight that performance degradation is not solely determined by delay length or frequency, but by the optimizer's ability to contextually reason over delayed information. The proposed DA-ADMM achieves consistently better coordination performance across a wide range of delay conditions, offering a principled and efficient mechanism for resilient multi-robot motion planning under imperfect communication.

Problem

• Developing resilient multi-robot motion planning under communication delays
• Adapting penalty parameters using real-time delay statistics for coordination
• Improving robustness and success rates in distributed optimization systems

Innovation

• Delay-Aware ADMM adapts penalty parameters using delay statistics
• Agents down-weight stale information and prioritize recent updates
• DA-ADMM improves robustness and success rate in simulations