Industrial robotics has shifted from rigid, task-specific tools to adaptive, intelligent systems powered by artificial intelligence (AI), machine learning (ML), and sensor integration, revolutionizing efficiency and human–robot collaboration across manufacturing, healthcare, logistics, and agriculture. Collaborative robots (cobots) slash assembly times by 30% and boost quality by 15%, while reinforcement learning enhances autonomy, cutting errors by 30% and energy use by 20%. Yet, this review transcends descriptive summaries, critically synthesizing these trends to expose unresolved tensions in scalability, cost, and societal impact. High implementation costs and legacy system incompatibilities hinder adoption, particularly for SMEs, while interoperability gaps—despite frameworks, like OPC UA—stifle multi-vendor ecosystems. Ethical challenges, including workforce displacement and cybersecurity risks, further complicate progress, underscoring a fragmented field where innovation outpaces practical integration. Drawing on a systematic review of high-impact literature, this study uniquely bridges technological advancements with interdisciplinary applications, revealing disparities in economic feasibility and equitable access. It critiques the literature’s isolation of trends—cobots’ safety, ML’s autonomy, and perception’s precision—proposing the following cohesive research directions: cost-effective modularity, standardized protocols, and ethical frameworks. By prioritizing scalability, interoperability, and sustainability, this paper charts a path for robotics to evolve inclusively, offering actionable insights for researchers, practitioners, and policymakers navigating this dynamic landscape.

In this article, we propose a new scheme for a fuzzy logic controller, which includes acceleration as one of its linguistic variables, as opposed to other techniques and approaches that have been developed and reported in the literature. This method is used for controlling the tracking of the trajectory followed by the joints of a 2-DoF manipulator robot. To this end, a complete simulation environment is developed through the MatLab/Simulink^® software. The dynamic model of the manipulator robot includes a vector that consists of the estimate of the friction forces present in the joints. Then, a controller based on fuzzy logic is designed and implemented for each joint. Finally, the performance of the developed system is assessed and then compared to the performance of a classic PID controller. The incorporation of the fuzzy variable acceleration significantly improved the system’s response.

This paper investigates spiking neural networks (SNN) for novel robotic controllers with the aim of improving accuracy in trajectory tracking. By emulating the operation of the human brain through the incorporation of temporal coding mechanisms, SNN offer greater adaptability and efficiency in information processing, providing significant advantages in the representation of temporal information in robotic arm control compared to conventional neural networks. Exploring specific implementations of SNN in robot control, this study analyzes neuron models and learning mechanisms inherent to SNN. Based on the principles of the Neural Engineering Framework (NEF), a novel spiking PID controller is designed and simulated for a 3-DoF robotic arm using Nengo and MATLAB R2022b. The controller demonstrated good accuracy and efficiency in following designated trajectories, showing minimal deviations, overshoots, or oscillations. A thorough quantitative assessment, utilizing performance metrics like root mean square error (RMSE) and the integral of the absolute value of the time-weighted error (ITAE), provides additional validation for the efficacy of the SNN-based controller. Competitive performance was observed, surpassing a fuzzy controller by 5% in terms of the ITAE index and a conventional PID controller by 6% in the ITAE index and 30% in RMSE performance. This work highlights the utility of NEF and SNN in developing effective robotic controllers, laying the groundwork for future research focused on SNN adaptability in dynamic environments and advanced robotic applications.

This research delves into the development and evaluation of two distinct controllers for a 3-DoF robotic arm in the context of Industry 4.0. Two primary control strategies are presented in the study. The first is a Fuzzy Logic Controller that utilizes joint position error and its derivative as inputs, employing a set of 9 control knowledge rules. The second is an Adaptive Neuro-Fuzzy Inference System (ANFIS) Controller, trained to learn the inverse dynamic model of the robot through a structured dataset. The research emphasizes the importance of accurate parameter tuning and data acquisition to achieve optimal control system performance. Extensive experimentation was conducted to evaluate the controllers’ performance in trajectory tracking and their response against external disturbances, such as load variations. The controllers exhibited remarkable precision and proficiency in tracking reference trajectories, with minimal deviations, overshoots, or oscillations. A quantitative analysis using performance indices such as root mean square error (RMSE) and the integral of the absolute value of the time-weighted error (ITAE) further confirmed the controllers’ effectiveness. Notably, the ANFIS Controller consistently outperformed the Fuzzy Logic Controller, demonstrating superior precision in trajectory tracking. The study underscored the importance of selecting the right control method and obtaining high-quality training data. Challenges in parameter tuning for Fuzzy Logic Controllers and potential time constraints in training ANFIS were discussed. The findings have significant implications for advancing robotic control systems, particularly in the era of Industry 4.0.

This paper presents the design and implementation of four control strategies applied to a real underactuated manipulator robot with 3-DOF (Degrees of Freedom). Additionally, an original methodology for controlled oscillatory compensations is designed and implemented to mitigate the effect of a passive joint on the overall performance of this manipulator robot. The objective of this methodology is to create controlled oscillations that allow the faulty link and its (passive) joint to physically align with their adjacent previous link. The implemented control techniques are sinh–cosh, neural compensation, gain scheduling PID, and gain scheduling sinh–cosh. The real robot in which these four control strategies and oscillatory compensation methodology are implemented is a SCARA (Selective Compliant Assembly Robot Arm) robot. To assess controller performance—once the 3-DOF underactuated manipulator robot starts its trajectory—after t = 4.5 s, a fault is activated in its joint No. 2, converting it into a passive joint. The performance indicators IA (index of agreement), RMS (Root Mean Square), and RSD (Residual Standard Deviation) are used to analyze, compare, and evaluate the behavior of the four control strategies and the compensation methodology.