Design and Implementation of a Cloud-Edge Collaborative Mapping and Control System for Virtual Mobile Robots: A Review

Abstract

The increasing use of autonomous mobile robots in warehouses, healthcare, manufacturing, logistics, agriculture, surveillance, and disaster response has created a need for systems that combine responsive local control with scalable remote services. Conventional robots execute mapping, localization, path planning, obstacle avoidance, monitoring, and data management on limited onboard hardware. Cloud-only designs provide elastic computation and centralized visibility but expose time-critical control to network delay and disconnection. This review studies robot middleware, simulation, simultaneous localization and mapping, autonomous navigation, cloud robotics, edge computing, and cloud-native deployment. Based on the synthesis, a Cloud-Edge Collaborative Mapping and Control System for Virtual Mobile Robots is proposed. ROS 2 Jazzy and Gazebo Harmonic form the virtual-robot and edge layers; SLAM, localization, and Nav2 retain the low-latency control loop; FastAPI and WebSockets provide cloud communication; Redis and PostgreSQL manage transient and durable data; React.js provides monitoring and control; and Docker with Kubernetes/Minikube supports reproducible deployment. The proposed architecture is simulation-first, cost-effective, modular, and suitable for academic validation before physical-robot integration. The paper defines its component boundaries, data flow, evaluation methodology, risks, and future extensions without presenting uncollected experimental results.