Advanced Power Converters and Learning in Diverse Robotic Innovation: A Review

Abstract

This paper provides a comprehensive review of the integration of advanced power management systems and learning techniques in the field of robotics. It identifies the critical roles these areas play in reshaping the capabilities of robotic systems across diverse applications. To begin, it highlights the significance of efficient power usage in modern robotics. The paper explains how advanced power converters effectively control voltage, manage current and shape waveforms, thereby optimizing energy utilization. These converters ensure that robotic components receive the precise voltage levels they require, leading to improved motor performance and enabling precise control over motor behavior. Consequently, this results in extended operational times and increased design flexibility. Furthermore, the review explores the integration of learning approaches, emphasizing their substantial impact on robotic perception, decision-making and autonomy. It discusses the application of techniques such as reinforcement learning, supervised learning and unsupervised learning, showcasing their applications in areas like object recognition, semantic segmentation, sensor fusion and anomaly detection. By utilizing these learning methods, robots become more intelligent, adaptable and capable of autonomous operation across various domains. By examining the interaction between advanced power management and learning integration, this review anticipates a future where robots operate with increased efficiency, adapt to various tasks and drive technological innovation across a wide range of industries.