The integration of intelligent robots in industrial production processes has the potential to significantly enhance efficiency and reduce human adversity. However, for such robots to effectively operate within human environments, it is critical that they possess an adequate understanding of their surroundings and are able to navigate through narrow aisles while avoiding both stationary and moving obstacles. In this research study, an omnidirectional automotive mobile robot has been designed for the purpose of performing industrial logistics tasks within heavy traffic and dynamic environments. A control system has been developed, which incorporates both high-level and low-level algorithms, and a graphical interface has been introduced for each control system. A highly efficient micro-controller, namely myRIO, has been utilized as the low-level computer to control the motors with an appropriate level of accuracy and robustness. Additionally, a Raspberry Pi 4, in conjunction with a remote PC, has been utilized for high-level decision making, such as mapping the experimental environment, path planning, and localization, through the utilization of multiple Lidar sensors, IMU, and odometry data generated by wheel encoders. In terms of software programming, LabVIEW has been employed for the low-level computer, and the Robot Operating System (ROS) has been utilized for the design of the higher-level software architecture. The proposed techniques discussed in this paper provide a solution for the development of medium- and large-category omnidirectional mobile robots with autonomous navigation and mapping capabilities.

The production of textiles has undergone a considerable transformation, progressing from its primitive origins in hand-weaving to the implementation of contemporary automated systems. Weaving yarn into fabric is a crucial process in the textile industry that requires meticulous attention to output quality products, particularly in the tension control section. The efficiency of the tension controller in relation to the yarn tension significantly affects the quality of the resulting fabric, as proper tension control leads to strong, uniform, and aesthetically pleasing fabric, while poor tension control can cause defects and yarn breakage, leading to production downtime and increased costs. Maintaining the desired yarn tension during textile production is crucial, although it poses several problems, such as the continuous diameter change of the unwinder and rewinder sections leading to system change. Another problem faced by the industrial operation is maintaining proper tension on the yarn while changing the roll-to-roll operation velocity. In this paper, an optimized method for controlling yarn tension through the cascade control of tension and position, incorporating feedback controllers, feedforward, and disturbance observers, has been proposed to make the system more robust and suitable for industrial use. In addition, an optimum signal processor has been designed to obtain sensor data with reduced noise and minimal phase difference.

In adapting to the demands of this modernized landscape, a conventional human-operated forklift within an industrial or warehouse setting falls short. However, the adoption of autonomous forklifts remains a distant prospect for many companies, primarily due to the formidable implementation and switching costs associated with artificial intelligence and complex control mechanisms. To bridge this gap, we present the development of a teleoperated forklift utilizing mecanum wheels for enhanced maneuverability. A key contribution of this work lies in the design of a novel synchronization method for the precise position control of the pallet carriers. This method surpasses the conventional independent and master–slave approaches, demonstrably achieving superior tracking and synchronization performance. Also, a model-based velocity control algorithm was designed for the mecanum wheels to facilitate the mobility of the system. The forklift was successfully able to carry a maximum load of 300 kg. For the comparison of the tracking and synchronization performance, the independent and master–slave methods were also applied to the system. The proposed method showed better performance compared to other structures.

Roll-to-roll (R2R) manufacturing processes demand precise control of web or yarn velocity and tension, alongside robust mechanisms for handling system failures. This paper presents an integrated approach combining high-performance control with reliable fault detection for an experimental R2R system. A model-based cascade control strategy is designed, incorporating system identification, radius compensation for varying roll diameters, and a Kalman filter to mitigate load sensor noise, ensuring accurate regulation of yarn velocity and tension under normal operating conditions. In parallel, a data-driven fault detection layer uses Gaussian Process Regression (GPR) models, trained offline on healthy operating data, to predict yarn tension and motor speeds. During operation, discrepancies between measured and GPR-predicted values that exceed predefined thresholds trigger an immediate shutdown of the system, preventing material loss and equipment damage. Experimental trials demonstrate tension regulation within ±0.02 N and velocity errors below ±5 rad/s across varying roll diameters, while yarn-break and motor-fault scenarios are detected within a single sampling interval (<100 milliseconds) with zero false alarms. This study validates the integrated system’s capability to enhance both the operational precision and resilience of R2R processes against critical failures.