Implementing Blockchain Technology in Robotic Decision Making

Abstract

Background: Big Data has permeated numerous technological fields, most notably in robotics, allowing astonishing improvements. Large volumes of data from many sources may increase robotic system functionality and flexibility. Objective: This article investigates Big Data's impacts on robotic learning, decision-making, and adaptability to explain how it increases robotic capabilities and gives a complete analysis of integrated data-driven robotic systems. Methods: A comprehensive review and synthesis of the literature was conducted to investigate the integration and deployment of Big Data in robotics. From databases and case studies and theoretical frameworks on Big Data analytics and robotics were explored. Results: Big Data analytics in robots enhances learning in predictive analytics and machine learning algorithms, boosting decision-making. Industrial robots and self-driving cars improve operational efficiency and flexibility by boosting data processing, anomaly detection, and real-time decision-making. Conclusion: Robots have become more flexible in decision-making using Big Data and robotics. Integration improves robotics and allows innovative applications across disciplines, allowing data-driven robotic advancement. The findings suggest using Big Data analytics to improve robots and study future applications, contributing to technological convergence and societal impacts.

Keywords

• big data
• robotics
• decision-making
• adaptability
• machine learning (ml)
• predictive analytics
• operational efficiency
• anomaly detection
• real-time processing
• technological convergence