Leveraging Boosting Algorithms for Accurate Battery Life Prediction in Autonomous Robots

Autonomous robots are self-operating systems equipped with advanced algorithms and sensors, enabling them to navigate, perform tasks, and make decisions independently. Precise battery longevity estimation is vital for their efficient operation, impacting decision-making and resource management. These robots adapt to changing conditions using machine learning techniques to optimize tasks, improve performance, and enhance efficiency. Widely used across sectors like healthcare, agriculture, and space, these systems benefit significantly from accurate energy management. While other machine learning models are effective for time-series data, they often require large datasets and extensive computational resources. Training deep learning models, in particular, is time-intensive and computationally expensive. Boosting algorithms, such as GBM, XGBoost, and AdaBoost, offer a more efficient solution by iteratively correcting errors and focusing on challenging cases, delivering better accuracy and computational efficiency for complex tasks like battery life prediction. This research applies Gradient Boosting Machines, XGBoost, and AdaBoost to predict battery life based on conditions and energy consumption. GBM combines decision trees, training each tree to address the errors of the previous one, while XGBoost enhances performance with faster and more efficient learning. AdaBoost uses simple decision trees to address misclassifications effectively. The models are evaluated for accuracy and efficiency, showcasing the potential of boosting algorithms to improve energy optimization in autonomous robots. This study highlights their capability to enhance predictive accuracy and optimize energy management across diverse sectors, ensuring more reliable autonomous operations. This advancement supports the development of more sustainable and efficient autonomous robotic systems.