Multiple modern robotic applications benefit from centralized cognition and processing schemes. However, modern equipped robotic platforms can output a large amount of data, which may exceed the capabilities of modern wireless communication systems if all data is transmitted without further consideration. This research presents a multi-agent, centralized, and real-time 3D point cloud map merging scheme for ceaselessly connected robotic agents. Centralized architectures enable mission awareness to all agents at all times, making tasks such as search and rescue more effective. The centralized component is placed on an edge server, ensuring low communication latency, while all agents access the server utilizing a fifth-generation (5G) network. In addition, the proposed solution introduces a communication-aware control function that regulates the transmissions of map instances to prevent the creation of significant data congestion and communication latencies as well as address conditions where the robotic agents traverse in limited to no coverage areas. The presented framework is agnostic of the used localization and mapping procedure, while it utilizes the full power of an edge server. Finally, the efficiency of the novel established framework is being experimentally validated based on multiple scenarios.

多种现代机器人应用受益于集中式认知和处理方案。然而，现代配备的机器人平台可以输出大量数据，如果不进一步考虑就传输所有数据，这可能会超出现代无线通信系统的能力。这项研究提出了一种多智能体、集中式、实时 3D 点云地图合并方案，用于不断连接的机器人智能体。集中式架构使所有代理能够随时了解任务，从而使搜索和救援等任务更加有效。集中式组件放置在边缘服务器上，确保低通信延迟，而所有代理都利用第五代（5G）网络访问服务器。此外，所提出的解决方案引入了通信感知控制功能，该功能可调节地图实例的传输，以防止产生严重的数据拥塞和通信延迟，并解决机器人代理在有限或无覆盖区域中穿越的情况。所提出的框架与所使用的定位和映射过程无关，同时它利用了边缘服务器的全部功能。最后，基于多种场景对所建立的新颖框架的效率进行了实验验证。