基于图神经网络的多机器人通信受限的无标签运动规划与预测控制
Graph Neural Planning and Predictive Control for Multi-Robot Communication-Constrained Unlabeled Motion Planning
AI总结：
本文提出一种分层框架，结合图注意力规划器和分布式非线性模型预测控制器，以解决多机器人在通信受限环境下同时分配目标和生成安全轨迹的问题，通过图神经网络方法实现可扩展的去中心化解决方案。
摘要：
多机器人同时分配目标并生成安全轨迹的无标签运动规划问题在许多协作任务中至关重要。最近的图神经网络方法提供了可扩展的去中心化解决方案，但依赖于简化动力学和模拟环境，忽略了现实部署中的关键挑战，如动态可行性和通信限制。为了解决这些差距，我们提出了一种分层框架，结合图注意力规划器（GATP）和分布式非线性模型预测控制器（NMPC）。GATP通过多机器人协作提供中间子目标，而NMPC在非线性动力学和执行约束下强制安全。我们评估了该框架在仿真和真实世界四旋翼实验中的性能。得益于注意力机制和最小通信需求，我们展示了在更大团队中改进的泛化能力、对通信延迟高达200毫秒的鲁棒性以及实用可行性，具有去中心化的板载推理。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
