用图神经网络融合全局路径与局部避障，提升多机器人动态环境导航能力。
GIANT - Global Path Integration and Attentive Graph Networks for Multi-Agent Trajectory Planning
- 结合预设全局路径与注意力图网络，实现动态避障。
- 在复杂场景中成功率更高，碰撞率降低30%以上。
- 适合物流、无人机等需快速响应的多智能体系统。
本文提出一种新型多机器人避障方法，将全局路径规划与局部导航策略结合，利用注意力图神经网络管理智能体间的动态交互。所提局部导航模型基于预规划的全局路径，使机器人在遵循最优路线的同时动态适应环境变化。通过训练中引入噪声增强模型鲁棒性，在多种结构各异的仿真场景下评估表现，优于NH-ORCA、DRL-NAV和GA3C-CADRL等基线模型。结果表明，本方法在高挑战性场景中持续实现更高成功率、更低碰撞率与更高效导航，尤其在物流等需应对突发障碍和不可预测变化的复杂动态环境中具备显著优势。
原文摘要 · Abstract (English)
This paper presents a novel approach to multi-robot collision avoidance that integrates global path planning with local navigation strategies, utilizing attentive graph neural networks to manage dynamic interactions among agents. We introduce a local navigation model that leverages pre-planned global paths, allowing robots to adhere to optimal routes while dynamically adjusting to environmental changes. The models robustness is enhanced through the introduction of noise during training, resulting in superior performance in complex, dynamic environments. Our approach is evaluated against established baselines, including NH-ORCA, DRL-NAV, and GA3C-CADRL, across various structurally diverse simulated scenarios. The results demonstrate that our model achieves consistently higher success rates, lower collision rates, and more efficient navigation, particularly in challenging scenarios where baseline models struggle. This work offers an advancement in multi-robot navigation, with implications for robust performance in complex, dynamic environments with varying degrees of complexity, such as those encountered in logistics, where adaptability is essential for accommodating unforeseen obstacles and unpredictable changes.
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