基于5G网络边缘计算优化机器人操作速度：来自选择性收获机器人的见解
Optimising robotic operation speed with edge computing over 5G networks: Insights from selective harvesting robots
AI总结：
本文提出E5SH系统，将5G边缘计算与草莓收获机器人集成，通过对比图像分割模型、网络与消息协议，实现峰值处理性能较嵌入式NJXN系统提升18倍以上，为低成本高速机器人收获提供新方案。
摘要：
自主机器人进行选择性收获将成为未来农业的一项关键使能技术。通货膨胀加剧和熟练劳动力短缺是推动用户接受机器人收获的驱动因素。例如，机器人草莓收获需要实时高精度果实定位、三维建图以及用于三维果簇操作路径规划。尽管工业界和学术界已开发出多种草莓收获机器人，但尚未有任何一种实现与人工成本持平。实现这一目标需要提高采摘速度（感知、控制和移动）、精度，并开发低成本机器人系统设计。我们提出面向选择性收获的5G边缘服务器（E5SH）系统，该系统将高带宽、低延迟的第五代（5G）移动网络集成到作物收获机器人平台中，我们将其视为未来机器人收获系统的使能器。我们还考虑了处理规模和速度与系统环境和能源成本的关系。本文提出了一种系统架构，并通过一系列实验的定量结果进行评估，这些实验比较了系统在不同架构选择下的性能，包括图像分割模型、网络基础设施（5G与WiFi）以及消息协议，如消息队列遥测传输（MQTT）和传输控制协议机器人操作系统（TCPROS）。我们的结果表明，E5SH系统实现了阶跃式峰值处理性能加速，比独立嵌入式计算Nvidia Jetson Xavier NX（NJXN）系统快18倍以上。
英文摘要：
Selective harvesting by autonomous robots will be a critical enabling technology for future farming. Increases in inflation and shortages of skilled labour are driving factors that can help encourage user acceptability of robotic harvesting. For example, robotic strawberry harvesting requires real-time high-precision fruit localisation, 3D mapping and path planning for 3-D cluster manipulation. Whilst industry and academia have developed multiple strawberry harvesting robots, none have yet achieved human-cost parity. Achieving this goal requires increased picking speed (perception, control and movement), accuracy and the development of low-cost robotic system designs. We propose the edge-server over 5G for Selective Harvesting (E5SH) system, which is an integration of high bandwidth and low latency Fifth Generation (5G) mobile network into a crop harvesting robotic platform, which we view as an enabler for future robotic harvesting systems. We also consider processing scale and speed in conjunction with system environmental and energy costs. A system architecture is presented and evaluated with support from quantitative results from a series of experiments that compare the performance of the system in response to different architecture choices, including image segmentation models, network infrastructure (5G vs WiFi) and messaging protocols such as Message Queuing Telemetry Transport (MQTT) and Transport Control Protocol Robot Operating System (TCPROS). Our results demonstrate that the E5SH system delivers step-change peak processing performance speedup of above 18-fold than a stand-alone embedded computing Nvidia Jetson Xavier NX (NJXN) system.
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。
