Graph Neural Planning and Predictive Control for Multi-Robot Communication-Constrained Unlabeled Motion Planning

2026-05-18

AI Summary

This work addresses the challenge of cooperative motion planning for multi-robot systems under communication constraints and unlabeled target assignment. The authors propose a hierarchical framework comprising a high-level graph attention task planner (GATP) for scalable target allocation and a low-level decentralized nonlinear model predictive controller (NMPC) that generates dynamically feasible trajectories. By integrating graph attention mechanisms with onboard decentralized inference, the approach maintains robust performance under communication delays up to 200 ms and limited bandwidth, while enabling generalization to large-scale robot teams. Extensive simulations and real-world quadrotor experiments demonstrate significant advantages in scalability, communication robustness, and practical deployability.

Abstract

The multi-robot unlabeled motion planning problem of concurrently assigning robots to goals and generating safe trajectories is central in many collaborative tasks. Recent Graph Neural Network methods offer scalable decentralized solutions but rely on simplified dynamics and simulation environments, overlooking key challenges of real-world deployment such as dynamic feasibility and communication constraints. To address these gaps, we propose a hierarchical framework that combines a Graph ATtention Planner (GATP) with a decentralized Nonlinear Model Predictive Controller (NMPC). GATP provides intermediate subgoals through multi-robot cooperation, and the NMPC enforces safety under nonlinear dynamics and actuation constraints. We evaluate our framework in both simulation and real-world quadrotor experiments. Thanks to attention mechanisms and minimal communication requirements, we demonstrate improved generalization to larger teams, robustness to communication delays up to 200 ms and practical feasibility with decentralized on-board inference.

Problem

• multi-robot motion planning
• communication constraints
• unlabeled planning
• dynamic feasibility
• decentralized control

Innovation

• Graph Neural Networks
• Nonlinear Model Predictive Control
• Multi-Robot Motion Planning
• Communication Constraints
• Decentralized Control