Machine Learning-Based Movement Scheduling and Management for Autonomous Mobile Robot Navigation
Keywords:
autonomous mobile robot navigation, Augmented Gradient Support Vector Machine (AG-SVM), movement scheduling, management, Histogram of Oriented Gradients (HOG)Abstract
Introduce autonomous mobile robot navigation in a few sentences, along with its significance in numerous industries. A discussion of the difficulties in attaining effective and adaptable movement scheduling and management for autonomous robots is required. Emphasize the advantages of using machine learning approaches to solve these problems. In this study, we recommend movement scheduling and management based on an Augmented Gradient Support Vector Machine (AG-SVM) for autonomous mobile robot navigation. Assemble a comprehensive dataset with historical information on the movements of mobile autonomous robots in different contexts. Gather data on the positions and speeds of the robots, the environment, the order of the tasks, and any pertinent sensor data. By removing outliers, dealing with missing values, and normalizing the data, we can clean and preprocess the acquired dataset. To extract pertinent features for the movement scheduling and management activity, if necessary, perform feature engineering. The dataset's most beneficial components that help with movement planning and management are taken out using the Histogram of Oriented Gradients (HOG). This technique helps to reduce dimensionality and improve the efficiency of learning algorithms. AG-SVM is used to manage and schedule movements. To improve the deployment of autonomous robots in various industries, it is important to emphasize the importance of adaptive and effective movement scheduling and management.
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