基于 5G 边缘计算的多无人艇协同避障系统的设计与仿真

Yinhui Rao, Yuanming Chen, Xiaobin Hong, Xiaodong Lin
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摘要

与单艘无人艇相比,多艘无人艇具有更加灵活的机动性和高效的任务完成能力,能够有效拓展任务类型。根据多无人艇协同作业的实际需求,提出了一种基于5G边缘计算的多无人艇协同避障方法,以实现多无人艇的统一规划和调度。首先,利用5G技术和Kubeedge边缘计算工具构建了基于云、边、端协同的多无人艇协同避障系统,并通过优化通信策略、构建高可用Kubeedge集群、构建港湾图像中心、使用Web管理接口等方式优化了Kubeedge边缘计算平台,进一步提高了系统的可靠性和稳定性。其次,利用基于云、边、端协同网络的YOLOR-Deepport多目标识别与跟踪算法完成障碍物目标的识别与跟踪任务,并设计了一套基于Kubedge集中控制平台的EECBS路径规划方法,为每艘无人艇实时规划无碰撞的高效路径。实验结果表明,与传统的无人艇自主规划避障方法相比,本文提出的协同规划避障方法在密集、狭窄场景下表现优异,导航路径更合理,航程缩小20%-50%,安全性更高。
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Design and Simulation of Multi Unmanned Boat Cooperative Obstacle Avoidance System Based on 5G Edge Computing
Compared to single unmanned boat, multi unmanned boats have more flexible mobility and efficient task completion capabilities, which can effectively expand the types of tasks. However, the traditional independent path planning and obstacle avoidance methods of unmanned boats are difficult to meet the requirements of collaborative operation among multiple unmanned boats due to the lack of information exchange. According to the actual demand of multi unmanned boats cooperative operation, a method of multi unmanned boats cooperative obstacle avoidance based on 5G edge computing is proposed to realize the unified planning and scheduling of multi unmanned boats. Firstly, 5G technology and Kubeedge edge computing tools are used to build a multi unmanned boat collaborative obstacle avoidance system based on cloud, edge and end collaboration, and the Kubeedge edge computing platform was optimized by optimizing communication strategies, building a highly available Kubeedge cluster, building a Harbor image center, and using Web management interfaces to further improve the reliability and stability of the system. Secondly, the YOLOR-Deepport multi target recognition and tracking algorithm based on cloud, edge and end collaborative network is used to complete the recognition and tracking tasks of obstacle targets, and a set of EECBS path planning methods based on Kubedge centralized control platform is designed to plan collision free and efficient paths for each unmanned boat in real-time. Finally, the effectiveness of the system was verified through simulation experiments. The experimental results show that compared to the traditional autonomous planning obstacle avoidance method for unmanned boats, the collaborative planning obstacle avoidance method proposed in this paper can exhibit excellent performance in dense and narrow scenarios, with a more reasonable navigation path, a range reduction of 20% - 50%, and higher safety. The results show that the cooperative obstacle avoidance system based on 5G edge computing designed in the paper is feasible, and it can effectively realize the cooperative path planning and obstacle avoidance of multi unmanned boats.
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