Abstract: Autonomous mobile robot navigation refers to the ability of robots to navigate and move in their environment without human intervention. It is of great significance in various industries as it enables robots to perform tasks efficiently and safely, leading to increased productivity and cost-effectiveness. Industries such as manufacturing, logistics, healthcare, and agriculture all benefit from the implementation of autonomous mobile robot navigation. An analysis of the challenges involved in achieving efficient and flexible movement planning and control for autonomous robots is necessary. Highlight the benefits of employing machine learning methodologies to address these issues. This work proposes the utilization of an Augmented Gradient Support Vector Machine (AG-SVM) to facilitate movement scheduling and management in the context of autonomous mobile robot navigation. Create a thorough dataset containing historical data on the locomotion of mobile autonomous robots in various scenarios. Collect data regarding the robots' positions and velocities, the surrounding environment, the sequence of jobs, and any relevant sensor information. To ensure data cleanliness and preprocessing, it is necessary to eliminate outliers, handle missing values, and normalize the acquired dataset. If needed, conduct feature engineering to extract relevant characteristics for the task of movement scheduling and management. The most advantageous elements of the dataset that aid in movement planning and management are extracted using the Histogram of Oriented Gradients (HOG). This method aids in decreasing dimensionality and enhancing the efficacy of learning algorithms. AG-SVM is utilized for the management and coordination of movements. In order to enhance the implementation of selfgoverning robots across different sectors, it is crucial to underscore the significance of adaptable and efficient movement scheduling and administration.Keywords: autonomous mobile robot navigation, Augmented Gradient Support Vector Machine (AGSVM), movement scheduling, management, Histogram of Oriented Gradients (HOG)