With the evolution of robotic technology, the expansion of operations into challenging environments underscores the growing need for effective teleoperation systems. In such an environment, robots or machines can improve the efficiency and safety of tasks by delivering more detailed and accurate information to workers through virtual reality (VR). Current teleoperation systems have limitations in providing a comprehensive understanding of the work environment. Accordingly, this study proposes a technology that utilizes VR to provide a high level of telepresence to workers and enable intuitive control. To achieve this, we introduce a pregenerated computer-assisted design model for static objects beyond the viewing area of RGB-D cameras and a method to update the point cloud of the target objects, which are dynamic objects, in real-time. By incorporating this information, we created a 3D visual map and delivered it to the operator in real-time through HMD, enabling the operator to clearly recognize the robot’s current location and surroundings. In addition, we introduced hand motion recognition through HMD viewpoints and VR controllers, allowing the operator to intuitively control the robot. These techniques can improve the efficiency and safety of remote work.

This paper introduces a novel approach for prosthetic wrist control, addressing limitations of traditional electromyography-based methods. While previous research has primarily focused on hand and gripper development, our study emphasizes the importance of wrist mobility for enhancing dexterity and manipulation skills. Leveraging a combination of visual data and inertial sensors, we proposed a system capable of estimating object orientation in real-time, enabling automatic and natural control of a prosthetic wrist. Our deep learning-based model can accurately interpret object posture from the user’s perspective, facilitating seamless wrist movement based on object inclination. In addition, Gaussian filtering was employed to mitigate noise in image-based posture estimation while preserving essential trends. Through this approach, users can achieve natural positioning without needing additional muscle movements, thus significantly improving prosthetic usability and user experience.

The Steer-by-Wire (SbW) system is a system that eliminates the physical connection structure of the steering system. Instead, it steers the tires through electrical communication that transmits the driver’s intention to the motor. However, the SbW system poses a significant risk in the event of a system failure. This highlights the need for effective failure backup strategies.In our study, we propose a new estimation technique. This technique accurately predicts the magnitude of the front wheel steering angle, which is determined by the vehicle motion. This prediction is particularly useful when rear wheel steering and differential braking are applied to facilitate vehicle steering in the event of a fatal SbW system failure. The estimation model is derived based on the single track model. To construct the prediction model using only measurable states, we replaced the side slip angle with the lateral acceleration signal. Additionally, we incorporated compensation for changes in cornering stiffness due to differential braking. This enhances the accuracy of the model. We validated the proposed steer angle estimation model in a virtual environment using CarSim SW and MATLAB/Simulink.

Environmental issues have become a global concern recently. Countries worldwide are making efforts for carbon neutrality. In the automotive industry, focus has shifted from internal combustion engine vehicle to eco-friendly vehicles such as Electric Vehicles (EVs), Hybrid Electric Vehicles (HEVs), and Fuel Cell Electric Vehicles (FCEVs). For driving strategy, research on vehicle driving method that can reduce vehicle energy consumption, called eco-driving, has been actively conducted recently. Conventional cruise mode driving control is not considered an optimal driving strategy for various driving environments. To maximize energy efficiency, this paper conducted research on eco-driving strategy for EVs-based on reinforcement learning. A longitudinal dynamics-based electric vehicle simulator was constructed using MATLAB Simulink with a road slope. Reinforcement learning algorithms, specifically Deep Deterministic Policy Gradient (DDPG) and Deep QNetwork (DQN), were applied to minimize energy consumption of EVs with a road slope. The simulator was trained to maximize rewards and derive an optimal speed profile. In this study, we compared learning results of DDPG and DQN algorithms and confirmed tendencies by parameters in each algorithm. The simulation showed that energy efficiency of EVs was improved compared to that of cruise mode driving.

Automated valet parking systems have been researched because they provide a good service condition for autonomous vehicles, with their limited space and unmanned environment. Previous parking algorithms focused on planning a path to a parking space based on geometry. However, this approach only works when the parking space is simple. To make automated parking algorithms useful in different environments, it is crucial to drive a path from the entrance to the target space and plan a safe parking path, taking into account the surrounding vehicles in the parking lot. This study organizes the structure of the automated valet parking system into two phases. The first phase involves driving from the origin to the destination. The second phase focuses on planning a path for parking the vehicle in the parking lot. It considers the position, orientation, and parking space to plan a path that aligns correctly. Simulation results demonstrate that the proposed algorithm can plan paths in various parking environments and park vehicles in narrow parking spaces. It is expected that this proposed automated valet parking algorithm can be further improved to contribute to the early commercialization of automated driving technology.

As advanced driver-assistance systems become more common in commercial vehicles, there is a growing need for evaluating safety of vehicles. Low platform target robot systems play a crucial role in this evaluation process as they can assess safety performances of autonomous vehicles. Driving stability of a target robot during real vehicle tests depends significantly on its suspension system. Therefore, developing an appropriate suspension device for the target robot is of utmost importance. This study aimed to improve driving stability by comparing two different suspension configurations: a single rocker and a double rocker, both incorporating a crank rocker mechanism. Initially, a two-dimensional model that met constraints of the suspension device was developed, followed by an analysis of reaction forces. Subsequently, an optimal design was determined using design of experiments principles based on parameters of a 2D model. The manufactured suspension system model based on the optimal design underwent multi-body dynamics simulation to evaluate driving stability. Comparative analysis of driving stability for both configurations was performed using MBD simulation, offering insights into the superior suspension design for the target robot.

The Technical Specification for Interoperability (TSI) legally mandates the prediction and verification process of the Reliability, Availability, Maintainability and Safety (RAMS) in signaling and communication systems. Recently, domestic regulations, including the Railroad Safety Act, have been strengthened in order to better meet the requirements for participating in international projects. To comply with these regulatory requirements, manufacturers and development organizations must prepare verification data pertaining to the reliability and safety of railway components and related systems. This paper aims to analyze the requirements of Failure Mode, Effects and Criticality Analysis (FMECA) through international laws and standards, and subsequently propose a compliant FMECA system for the domestic railway industry. The proposed FMECA system is then compared with the analysis results of actual failure data to determine its suitability for establishing a Reliability, Availability, Maintainability (RAM) verification standard for railway products in relation to conformity assessment.

The hyperloop is a revolutionary form of ground transportation that can reach speeds of up to 1,200 ㎞/h. However, there is a challenge with the superconducting electromagnets used in its propulsion and levitation systems. These magnets generate strong magnetic fields, which can create resistance when they interact with the surrounding structures, including the vacuum tubes. Therefore, it is important to study this magnetic resistance and understand how it affects the acceleration of the hyperloop vehicles. This research aims to analyze the changes in magnetic drag force near the junctions of vacuum tubes, particularly when these tubes are made of identical or different materials.

The key components of smart manufacturing, a central concept in the era of the 4th Industrial Revolution, consist of digital twin technology, AI, and computer vision technology. In this study, these technologies were utilized to govern the Poppy robot, a humanoid robot designed for educational and research purposes. The digital twin creates a virtual environment capable of real-time simulation, analysis, and control of the robot’s motions. The digital twin of the robot was constructed using Unity, a 3D development program. Motion data was captured while simulating the physical structure and movements of the virtual robot. This data was then fed into a Tensorflow-based deep neural network to generate a regression modelthat predicts motor rotation based on the position of the robot’s hand. By integrating this model with a Python-based robot control program, the robot’s movements could be effectively managed. Additionally, the robot was controlled using Openpose, a computer vision algorithm that predicts characteristic points on a human body. Position data for human joint points was collected from 2D images, and the motor angle was calculated based on this data. By implementing this approach on an actual robot, it became possible to enable the robot to replicate human movements.

Induction heating is a technology that uses heat generated by resistance when a high-frequency current is applied to a coil. An electric range using this is called an Induction Heating (IH) electric range. IH electric ranges are being widely applied in commercial products recently because they have higher thermal efficiency performances than other methods. The performance of a heating coil of an IH electric range greatly varies depending on the shape and number of coils. Thus, research on optimal coil shape and number according to product shape is required. Therefore, this study aimed to design an optimal heating coil at the set temperature of an electric range product. Target temperature was set to the temperature that a commercial stainless-steel container could withstand. The thickness of the coil copper wire, the number of windings, the applied voltage, and the frequency were set as design variables. A sensitivity analysis was performed to check the influence of each design variable on coil temperature. Based on this, optimal design was performed using the response surface method. Electromagnetic field-thermal analysis was performed with the designed coil and a very approximate result was obtained with a 0.07% error from the set target temperature.