A Comprehensive Review of Machine Learning Algorithms in Autonomous Robotics: Challenges and Future Prospects

Abstract

Autonomous robots have been developed in many fields and presented various solutions that can help industries with AI, ML, and robotics to improve the quality, flexibility, and security of their operations. This work seeks to review how self-organizing autonomous robots utilize machine learning algorithms to assist in their navigation, object identification, and decision-making and location. A thorough literature analysis shows the amalgamation of deep reinforcement learning, computer vision, and real-time planning algorithms that allow robots to perform successfully in dynamic and uncertain conditions. The methodology includes an analysis of state-of-the-art systems, case studies, and expert insights, providing a holistic view of the challenges and opportunities in autonomous robotics. Key findings reveal that while ML-driven systems significantly improve autonomous capabilities, issues such as safety, ethical concerns, and real-time decision making remain critical areas for research. This study contributes to the growing body of knowledge by synthesizing recent advancements and identifying pathways for future innovation, paving the way for smarter and more adaptable autonomous robots.