借助5G边缘计算实现远程全身控制
Enabling Remote Whole-Body Control with 5G Edge Computing
摘要：
现实世界的应用需要重量轻、节能、完全自主的机器人。然而，不断增加的自主性往往是不断升级的计算需求的同义词。因此，可能需要将密集的计算-不仅是传感和规划，而且还有低水平的全身控制-卸载到远程服务器，以减少机载计算需求。第五代（5G）无线蜂窝技术凭借其低延迟和高带宽功能，有可能解锁复杂机器人的基于云的高性能控制。然而，足式机器人的最先进控制算法只能容忍非常低的控制延迟，即使是超低延迟的5G边缘计算有时也无法实现。在这项工作中，我们研究了5G链路上足式机器人的基于云的全身控制问题。我们提出了一种新的方法，包括一个标准的基于优化的控制器的网络边缘和一个局部线性，近似最优的控制器，显着降低了板载的计算需求，同时增加了鲁棒性延迟和可能的通信损失。包括真实5G通信模型的人形平衡和行走任务的模拟实验表明，在5G无线链路中可能遇到的抖动和延迟情况下，机器人运动的可靠性得到了显着提高。
英文摘要：
Real-world applications require light-weight, energy-efficient, fully autonomous robots. Yet, increasing autonomy is oftentimes synonymous with escalating computational requirements. It might thus be desirable to offload intensive computation--not only sensing and planning, but also low-level whole-body control--to remote servers in order to reduce on-board computational needs. Fifth Generation (5G) wireless cellular technology, with its low latency and high bandwidth capabilities, has the potential to unlock cloud-based high performance control of complex robots. However, state-of-the-art control algorithms for legged robots can only tolerate very low control delays, which even ultra-low latency 5G edge computing can sometimes fail to achieve. In this work, we investigate the problem of cloud-based whole-body control of legged robots over a 5G link. We propose a novel approach that consists of a standard optimization-based controller on the network edge and a local linear, approximately optimal controller that significantly reduces on-board computational needs while increasing robustness to delay and possible loss of communication. Simulation experiments on humanoid balancing and walking tasks that includes a realistic 5G communication model demonstrate significant improvement of the reliability of robot locomotion under jitter and delays likely to experienced in 5G wireless links.
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