用云平台调度复杂机器人控制，动态应对机器人数量变化。
Cloud-Based Scheduling Mechanism for Scalable and Resource-Efficient Centralized Controllers
- 基于Kubernetes的调度机制实时管理多机器人控制计算
- 在机器人数量变化时仍保持稳定性能，计算负载有效分担
- 适合需要弹性扩展的大型机器人集群系统
本文提出一种新型方法，解决大规模系统中部署复杂机器人软件的挑战，即针对多智能体系统的集中式非线性模型预测控制器（CNMPCs）。该方法基于Kubernetes的调度机制，用于监控和优化CNMPCs的运行，并克服集中式控制方案的可扩展性限制。通过在实时云环境中利用集群资源，所提机制有效卸载了CNMPCs的计算负担。实验表明，该系统在机器人数量动态变化的场景下依然表现出色，验证了其有效性与高性能。本工作推动了基于云的控制策略发展，为云控机器人系统的性能提升奠定基础。
原文摘要 · Abstract (English)
This paper proposes a novel approach to address the challenges of deploying complex robotic software in large-scale systems, i.e., Centralized Nonlinear Model Predictive Controllers (CNMPCs) for multi-agent systems. The proposed approach is based on a Kubernetes-based scheduling mechanism designed to monitor and optimize the operation of CNMPCs, while addressing the scalability limitation of centralized control schemes. By leveraging a cluster in a real-time cloud environment, the proposed mechanism effectively offloads the computational burden of CNMPCs. Through experiments, we have demonstrated the effectiveness and performance of our system, especially in scenarios where the number of robots is subject to change. Our work contributes to the advancement of cloud-based control strategies and lays the foundation for enhanced performance in cloud-controlled robotic systems.
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