The main aim of this study was to decrease the weight of mobile transportation robots and to check the optimized shape using topology optimization and structural analysis. The analyses were applied using CAE Software with the methods of topology optimization and structural analysis. In the topology optimization process, the preserved areas were defined for connection and fixing areas. The aim was to decrease the weight of the robot structure and lower the energy consumption of the robotic system. After topology optimization, the structural strength analysis was applied to the new optimized structure to check the strength. The weight of the structure was decreased with a ratio of 20%. In addition to this, the structural strength of the robot was observed similar to the original body with the factor of safety value as three.

In this study, structural and dynamic analyses were applied to the mobile transportation robot to find the structural strength and to calculate the power and torque requirements. The mobile transportation robot was designed using CAD software as two modules; mecanum wheeled locomotion module and robotic arm module, respectively. The analyses were carried on the CAE Software using the static structural and rigid body dynamics tools. In the structural analysis, the weight of the robot and the additional load (98.1 N) were applied to the body of the robot. The material of the robot body was selected as Aluminum 6061 O sheet metal. In the dynamic analysis, the operation environment and conditions were simulated in the CAE software. The structural analysis results showed that the factor of safety value was obtained as 10.282 under applied loads and this value indicates that the chassis of mobile transportation robot is supposed to carry loads reaching up to 30 kg without any functional and mechanical problem. Furthermore, from the dynamic analysis, the total torque requirement of the mobile robot was about 16.56 Nm.

In the world of Industry 4.0, Autonomous Mobile Robots (AMRs) are now vital parts of modern industrial automation. This study examines how the Robot Operating System (ROS) plays a crucial role in advancing technology for AMRs. By looking at real-life examples, it shows how ROS helps in creating and using AMRs, changing how industrial processes work. The study demonstrates how ROS is being integrated into AMR design and operation, leading to improved autonomy, flexibility, and productivity in industrial settings. This study discusses how ROS-powered AMRs have transformed various tasks like material handling, warehouse logistics, and autonomous navigation, leading to increased productivity and cost-efficiency. It also explores the challenges and opportunities brought about by ROS in the Industry 4.0 era, including sensor fusion, machine learning, and human-robot teamwork. Furthermore, ROS not only influences the design and operation of AMR, but also enables smooth integration with advanced technologies such as sensor fusion and machine learning. This opens up opportunities for improved flexibility and teamwork between humans and robots in the ever-evolving environment of Industry 4.0. The importance of ROS in connecting traditional manufacturing practices with the changing demands of the fourth industrial revolution is emphasized.

This paper presents a measurement-driven study of end-to-end latency in an indoor private standalone 5G network deployed at TU Wien IFT TEC-Lab. The testbed combines pico radio units, edge computing resources, and a local 5G core to form a campus-scale private network architecture designed for low-latency communication. To characterize network performance, TCP throughput and UDP one-way latency measurements were collected in a single-user, constant-bit-rate downlink setting at seven predefined indoor locations using iPerf-based tests at six traffic levels (1–500 Mbps), with five repetitions per condition. Based on these measurements, a compact parametric model was calibrated for this deployment to describe latency as a function of achieved bandwidth and location-dependent effects. The results show that latency remained low and relatively stable at low and medium traffic levels, generally staying below 20 ms between 1 and 200 Mbps, but increased more strongly as the operating point approached the practical throughput limit of the setup. The fitted model captured the overall latency trend with an in-sample MAE of 2.52 ms, an RMSE of 3.13 ms, and an $R^2$ of 0.842, while retaining comparable predictive performance under a trial-based test split ($R^2=0.835$) and leave-one-location-out validation ($R^2=0.818$). The compact model also achieved lower out-of-sample errors than the minimal M/M/1-type and polynomial-regression baselines under both validation schemes. Overall, the findings indicate that traffic load was the main driver of latency growth in the studied environment, while spatial effects remained measurable but secondary. The resulting formulation should be understood as an interpretable empirical model of end-to-end latency for one indoor private standalone 5G deployment under single-user, constant-bit-rate downlink conditions, rather than as a general latency model for private 5G networks. Within that scope, the model provides an interpretable description of the measured latency behavior and a basis for preliminary capacity assessment in this deployment. Its applicability to another private 5G network has not been established and would require a new measurement campaign, complete parameter re-estimation, and independent validation.