As service robots become more prevalent in social settings, there is a growing imperative for research in the realm of social behavior development for these machines. An essential aspect of attaining autonomy in mobile service robots is their capacity to perceive and understand their surroundings effectively. Despite being a formidable challenge, researchers have developed numerous solutions to empower robots with the ability to engage with and gain a deeper understanding of their environment. This enhanced capability endows robots with the intelligence required to make informed decisions by harnessing the knowledge they have accumulated. In our research study, we have deployed a robotic system equipped with an autonomy solution that seamlessly incorporates human intentions via effective human-robot interactions. To be more precise, we utilize vision-based human pose estimation to identify gestures indicative of human intentions. Subsequently, these intentions are translated into predefined commands, facilitating the autonomous scheduling of tasks for the robotic system. Our research provides a comprehensive overview of an autonomy solution tailored for mobile robots. It elaborates our approach to Gesture-based Robot Control and Navigation operations, leveraging human posture knowledge. Our key focus areas of emphasis encompass diverse techniques for acquiring knowledge, managing decisions, enhancing situational awareness, facilitating humanrobot interaction, and strategic planning. Additionally, we present the findings derived from the experimental phase of physically implementing the robot.