Song Byoung-Youl, Choi Hoon
Electronics and Telecommunication Research Institute, Daejeon 34129, Republic of Korea.
Department of Artificial Intelligence, Chungnam National University, Daejeon 34134, Republic of Korea.
Sensors (Basel). 2024 Sep 29;24(19):6297. doi: 10.3390/s24196297.
As the adoption of large-scale model-based AI grows, the field of robotics is undergoing significant changes. The emergence of cloud robotics, where advanced tasks are offloaded to fog or cloud servers, is gaining attention. However, the widely used Robot Operating System (ROS) does not support communication between robot software across different networks. This paper introduces ROS Gateway, a middleware designed to improve the usability and extend the communication range of ROS in multi-network environments, which is important for processing sensor data in cloud robotics. We detail its structure, protocols, and algorithms, highlighting improvements over traditional ROS configurations. The ROS Gateway efficiently handles high-volume data from advanced sensors such as depth cameras and LiDAR, ensuring reliable transmission. Based on the rosbridge protocol and implemented in Python 3, ROS Gateway is compatible with rosbridge-based tools and runs on both x86 and ARM-based Linux environments. Our experiments show that the ROS Gateway significantly improves performance metrics such as topic rate and delay compared to standard ROS setups. We also provide predictive formulas for topic receive rates to guide the design and deployment of robotic applications using ROS Gateway, supporting performance estimation and system optimization. These enhancements are essential for developing responsive and intelligent robotic systems in dynamic environments.
随着基于大规模模型的人工智能的应用不断增加，机器人技术领域正在经历重大变革。云机器人技术的出现，即将高级任务卸载到雾节点或云服务器，正受到关注。然而，广泛使用的机器人操作系统（ROS）不支持跨不同网络的机器人软件之间的通信。本文介绍了ROS网关，这是一种中间件，旨在提高ROS在多网络环境中的可用性并扩展其通信范围，这对于在云机器人技术中处理传感器数据非常重要。我们详细介绍了它的结构、协议和算法，突出了相对于传统ROS配置的改进。ROS网关能够高效处理来自深度相机和激光雷达等高级传感器的大量数据，确保可靠传输。基于rosbridge协议并在Python 3中实现，ROS网关与基于rosbridge的工具兼容，并在基于x86和ARM的Linux环境上运行。我们的实验表明，与标准ROS设置相比，ROS网关显著提高了诸如主题速率和延迟等性能指标。我们还提供了主题接收速率的预测公式，以指导使用ROS网关的机器人应用程序的设计和部署，支持性能估计和系统优化。这些增强对于在动态环境中开发响应式和智能机器人系统至关重要。
