用图神经网络和模型预测控制解决多机器人通信受限下的协同避障问题
Graph Neural Planning and Predictive Control for Multi-Robot Communication-Constrained Unlabeled Motion Planning
- 分层架构：图注意力规划器生成中间目标，非线性模型预测控制器保障安全
- 实测支持200毫秒通信延迟，可扩展至更大机器人团队
- 适合需要低通信开销的分布式无人机协同场景
多机器人无标签运动规划问题——即同时分配机器人到目标并生成安全轨迹——在诸多协作任务中至关重要。现有图神经网络方法虽具备可扩展的分布式求解能力，但依赖简化动力学与仿真环境，忽略了真实部署中的动态可行性与通信约束。为此，我们提出一种分层框架，结合图注意力规划器（GATP）与分布式非线性模型预测控制器（NMPC）。GATP通过多机器人协作生成中间子目标，NMPC则在非线性动力学与执行器约束下确保安全性。我们在仿真与真实四旋翼实验中验证了该框架。得益于注意力机制与极低通信需求，系统展现出对更大团队的泛化能力，能容忍高达200毫秒的通信延迟，并实现基于机载设备的分布式实时推理。
原文摘要 · Abstract (English)
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.
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