Autonomous robots are commonly operated on rough roads. Thus, it is essential to predict their dynamic characteristics. Even though it is possible to use real hardware to acquire a robot’s dynamic characteristics, this requires a significant amount of time and cost. Therefore, a real-time remote driving simulator must be developed to reduce these risks. Most real-time simulators employ physics engines, which are calculated using simple functional expressions based on particles. However, in this case, there is a limit to reflecting the dynamic characteristics of actual robots. In this study, a multi-body dynamic model of a robot was established. MATLAB Simulink was used to connect the vehicle model with the joystick and calculate user input signals. The PID control system determines the driving torque of the robot to satisfy the calculated signal. Gain value optimization is performed to enable real-time control. This study can be available to analyze the traversability.

This study proposes a path-tracking algorithm based on feed-forward (preview distance control) and feedback (LQR, linear quadratic regulator) controllers to reduce heading angle errors and lateral distance errors between a predefined path and an autonomous vehicle. The main objective of path-tracking is to generate control commands to follow a predefined path. The feed-forward control is applied to solve heading angle errors and lateral distance errors in the trajectory caused by curvatures of the road by controlling the steering angle of the vehicle. An LQR was applied to decrease the errors caused by environmental and external disturbances. The proposed algorithm was verified by simulating the driving environment of an autonomous vehicle using a CARLA simulator. Safety and comfort were demonstrated using the test vehicle. The study also demonstrated that the tracking performance of the proposed algorithm exceeded that of other path-tracking algorithms, such as Pure Pursuit and the Stanley Method.

Recently, industrial manufacturing has developed into additive manufacturing, benefiting from multi-item small-sized production and effective manufacturing. Importantly, Wire Arc Additive Manufacturing, which uses metal wires, is attracting worldwide attention for its high-quality metal product technology. Technological innovation that combines virtual physics with reality through big data communication, such as process variables along with Wire Arc Additive Manufacturing, is an essential task for implementing smart manufacturing technology. Due to the characteristic of Wire Arc Additive Manufacturing, numerous variable conditions exist, making it difficult to standardize robot"s process path data generation algorithms and data application methods, and this data generation method is being studied as a core element technology. The present study generated foundation process implementation, simulation, and generated path data for robots in virtual space using RoboDK, which provides robot libraries from multiple manufacturers, and Python, which is a universal programming language. To implement the experimental data in practice, ABB"s industrial six-axis robots IRB-6700 and Fronius TPS500i were used to control the arcing plasma heat source, and the process path worked the same as simulation. Based on the underlying experimental results, this process can be applied to generation of additive manufacturing in the Wire Arc Additive Manufacturing process for 3D models.

This paper presents a novel method of designing an efficient locomotion pattern generating algorithm for snake robots by a genetic algorithm (GA). In search and rescue operations in disaster areas, a snake robot requires multiple locomotion patterns. To overcome the complexity of snake robot control, we used a central pattern generator (CPG)-based control method which mimics the motion of a biological snake. GA was used to optimize CPG parameters to maximize locomotion performance. The locomotion performance according to the CPG parameters change was analyzed using the snake robot simulator. The proposed locomotion pattern generation algorithm evolved quickly for the target performance and obtained CPG parameters for the desired locomotion.