发表机构
Indiana University Bloomington（印第安纳大学布鲁明顿分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现实环境中机器人车队的车辆路径问题，提出HALO混合方法，利用异构图神经网络分离分配与路径规划，在部分可观测环境下优于启发式基线，并在静态VRP上超越最先进架构，实现快速实时部署。
AI 中文摘要
可扩展的机器人车队在包裹递送、仓库管理和军事行动等各种应用中变得越来越流行。先前的车队控制算法在受控环境中解决了包含多达1,000个任务的集中式路径规划问题，但它们未能考虑现实约束，例如分散式车队中典型的有限观测和通信范围。因此，将现有的车队控制算法部署到现实环境中目前是不可行的。为了解决这一问题，我们提出了基于局部观测的异构分配（HALO）方法来解决车辆路径问题（VRP）。HALO是一种混合方法，将VRP分解为分配和路径规划两部分，为动态环境中的机器人提供机载、实时的解决方案。在分配阶段，HALO利用异构图神经网络框架，通过独特的消息传递层来明确分离空间分布的学习和任务到机器人的兼容性。在部分可观测的在线VRP变体上的评估结果表明，HALO显著优于启发式基线，同时保持与全知离线HALO变体相似的解决方案质量。尽管HALO是为部分可观测环境明确设计的，但它对观测空间没有严格的上限，这使我们能够在传统的静态、单仓库VRP上测试HALO。在此，HALO的性能比专门为静态VRP变体优化的最先进架构高出高达14.06%。在所有测试中，该框架保持了最快的执行时间，这凸显了其在大规模实时部署中的潜力。
英文摘要
Scalable robotic fleets have become increasingly popular for various applications such as package delivery, warehouse management, and military operations. Prior fleet control algorithms solve centralized routing problems with up to $1{,}000$ tasks in controlled environments, yet they fail to consider realistic constraints such as limited observation and communication ranges typical of decentralized fleets. Thus, deploying existing fleet control algorithms into real-world settings is currently infeasible. To tackle this, we propose Heterogeneous Allocation via Localized Observations (HALO) to solve the Vehicle Routing Problem (VRP). HALO is a hybrid method that splits the VRP into allocation and routing portions to provide onboard, real-time solutions to robots in dynamic environments. During the allocation phase, HALO utilizes a heterogeneous graph neural network framework with unique message passing layers to explicitly separate the learning of spatial distributions and task-to-robot compatibility. Evaluation results on a partially observable, online variant of the VRP show HALO significantly outperforms the heuristic baseline while maintaining similar solution quality to an all-knowing offline variant of HALO. While HALO is explicitly designed for partially observable environments, it imposes no strict upper bound on the observation space allowing us to test HALO on the traditional static, single-depot VRP. Here, HALO outperforms state-of-the-art architectures strictly optimized for the static variant of the VRP by up to $14.06\%$. Throughout all testing, this framework maintains the quickest execution times which emphasizes its potential for large-scale, real-time deployment.
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