Design and Implementation of Cloud-Edge Collaborative Mapping and Control System for Mobile Robots

With the increasing complexity of mobile robot systems, multi-robot collaboration has become essential. However, such collaboration places heavy demands on both computational power and communication bandwidth. Cloud computing offers elastic and scalable infrastructure, allowing robotic functions to be encapsulated and delivered as on-demand services. Meanwhile, edge computing provides low-latency processing near the data source. The integration of these paradigms enables efficient coordination between cloud and edge infrastructures, forming a solution for real-time, scalable, and intelligent robotic control. This paper presents a cloud-edge collaborative system for mobile robots. Based on cloud-native technologies, the cloud control platform is implemented on Kubernetes (K8s) to orchestrate containerized control services, enabling dynamic resource allocation and automated deployment. An edge offloading mechanism is designed to support real-time data exchange and task coordination between robots and the cloud. Furthermore, a collaborative mapping and control framework is developed, where computationally intensive tasks such as map construction and trajectory generation are offloaded to the cloud, while real-time control and data pre-processing are executed at the edge. Experimental results in outdoor environments verify that the proposed framework significantly enhances system scalability and performance. These findings confirm the feasibility and generality of the proposed approach, highlighting its strong potential for large-scale deployment in cloud-based robotic systems.