Autonomous mobile robots (AMRs) have demonstrated significant potential for addressing a variety of real-world applications, ranging from logistics and inspections to construction and service tasks. Although modern AMRs are equipped with advanced mobility and task execution capabilities, their deployment remains largely confined to repetitive and pre-defined processes with limited flexibility. The challenge lies in enabling robots to dynamically interpret missions, manage context-dependent variables, and autonomously activate their capabilities in complex and unstructured environments. This study presents Auto-Mission, a solution-oriented intelligent control methodology that bridges user-defined missions and autonomous execution for mobile robots. Unlike the capability-oriented research, which advances specific robotic technologies, Auto-Mission addresses the practical challenge for enabling end users to define and deploy autonomous missions without close human supervision. This method systematically integrates existing capabilities, navigation, localization, and task execution into a coherent framework that is platform-independent, with the platform adaptation required only at the mission execution layer. Auto-Mission is built on a location-based map framework, where a mission is defined as a list of destinations coupled with specific tasks to be performed at each location. By leveraging spatial and accessibility data from the map, Auto-Mission computes optimal navigation paths, simulates mission workflows, and manages the real-time execution of tasks. Unlike rigid pre-recorded automation schemes, Auto-Mission introduces adaptability by dynamically integrating robot states, environmental contexts, and mission-specific goals. The methodology is designed to be universally applicable across diverse AMR platforms, with the customization required only at the mission execution layer for robotspecific commands and operational data handling. This study details the architecture of Auto-Mission, its integration with mobile robotic systems, and its potential to significantly enhance the deployment flexibility of AMRs in practical applications.