Collaborative Robots in Industry: A Review of Perception, Control, and Learning Strategies

Abstract

Collaborative robots, or cobots, have emerged as a transformative technology in modern industrial settings, enabling safer and more flexible interactions between humans and machines. Unlike traditional industrial robots that operate in isolated zones, cobots are designed to work alongside human workers, combining the precision and efficiency of automation with the adaptability of human collaboration. This paper presents a comprehensive review of the core strategies that enable collaborative functionality in industrial robots, focusing on perception, control, and learning. Perception is a fundamental capability that allows cobots to understand and respond to their environments. Through sensors, computer vision, and sensor fusion, cobots can detect objects, recognize human presence, and adapt to dynamic changes on the factory floor. Effective perception is essential for ensuring safety, avoiding collisions, and maintaining task accuracy. Control strategies are equally vital for managing physical interactions in shared workspaces. Techniques such as impedance control, force control, and hybrid control schemes are used to regulate robot movement in real time. These methods ensure smooth cooperation, responsive behavior, and compliance with safety standards, especially during physical human-robot interaction. Learning strategies further enhancecobot adaptability by enabling them to improve performance over time. Machine learning approaches, including reinforcement learning, supervised learning, and imitation learning, are applied to teach cobots how to perform new tasks, adjust to variations in workflows, and respond to unforeseen scenarios. This review outlines recent developments in these three key domains and evaluates their integration in industrial applications. It also identifies current challenges and future research directions aimed at improving cobot intelligence, usability, and safety. By bridging perception, control, and learning, this paper provides insights into the development of next-generation collaborative robots that are not only functional but also intuitive and reliable partners in industrial environments.