Informatics

Collaborative robots are machines that work hand in hand with humans; or as the name suggests, collaborate with them in a specific workspace. These robots are not enclosed in confined safety zones like traditional robots, as they interact very closely with humans. Though this is the case, appropriate measures are captivated while designing these robots considering human safety. These robots are well-versed in adapting to changes and frequent upgrades. They are flexible enough to carry out complex tasks. Due to these abilities, they become a significant asset in the manufacturing field. It’s been many years now since cobots are introduced in the industry sector. So, this is the right time to review various applications of cobots in manufacturing. First, the paper starts with a brief introduction followed by an extensive literature review which was structured after reviewing 76 research papers and articles. It ends with some essential conclusions. This paper discusses the diverse applications of cobots used in the manufacturing sector and their advantages. Further, it highlights the future of cobots and how they will be a boon for a technology-driven world.

This paper presents an artificial intelligence algorithm responsible for the autonomy of a platform. The proposed algorithm allows the platform to move from an initial position to a set one without human intervention and with understanding and response to the dynamic environment. The implementation of such a task is possible by using a combination of a camera identifying the environment with a laser LIDAR sensor and a vision system. The signals from the sensors are analysed through convolutional neural networks. Based on AI inference, the platform makes decisions, including determining the optimal path for itself. A transfer learning method will be used to teach the neural network. This article presents the results of learning the applied neural algorithm.

With the rapid developments of Industry 4.0 and Smart Manufacturing, customized manufacturing has been becoming greatly needed. Meanwhile, the challenge of production automation has become more bigger, especially for the automation of moving, picking, placing and manipulating objects. Many researchers have begun to work on Autonomous Ground Vehicles (AGVs). Most AGVs were utilized to carry middle or small objects, as the high-payload AGVs were rarely developed. This paper focused on the design of a High-Payload Mecanum-Wheel Ground Vehicle (MWGV), which was 1.7 m wide and 2.04 m long. The weight of the vehicle was 740 kg and it was able to carry the payload as its own weight (i.e. around 7,300 N). The safety factor of the structural strength was greater than 1.66 and the safety factor of the axial design was at least 6.24. The vehicle was designed to carry 150-kg weight with a reach of 1.375 m without falling. The design of Mecanum wheels provided great flexibility on movement with small rotational radius. Mathematical descriptions about how Mecanum wheels were controlled was also introduced in this paper. Furthermore, the mechatronics and software integrations were demonstrated. The final experimental results showed the developed MWGV was able to perform the desired movement properly.

This paper presents an integrative review of control strategies in robotics, covering classical control methods (linear quadratic regulator, proportional-integral-derivative), modern methods (adaptive, sliding mode, model predictive, and H-infinity), intelligent control methods (neural network, fuzzy logic, and machine learning), and hybrid control methods (integration of classical, modern, and intelligent control methods) to identify the advantages, limitations and gaps for future. A brief comparison of control methods between the types of control strategies is conducted with respect to robustness, stability, and complexity of implementation on 3 different levels of evaluation criteria: high, average, and low; advantages; limitations; and robotic applications, including examples. This paper discusses the theoretical and practical advancements and the classification of control strategies according to controller types (linear, nonlinear, and learning-based), approaches (model-based and model-free), and classifications (centralized, decentralized, and modal control). The review highlights the strengths, limitations, and potential research directions in bridging classical, modern, intelligent, and hybrid control paradigms to achieve safe, efficient, and adaptive robotic behavior in complex, uncertain environments. We discuss the future direction: autonomy, human-robot collaboration, and enhanced learning and challenges: cost, reliability, safety of control strategies, concluding with recommendations for future research.