The residual vibration when a robot manipulator is operated at high speed needs to be suppressed. These vibrations are generated by the resonance of a flexible object being moved by the robot, and research on control algorithms and motion profiles is ongoing to reduce them. In this paper, we propose a method to reduce the residual vibration of an object moved by a robot manipulator by optimizing the acceleration/deceleration time calculated using the object’s natural frequency. The relationship between acceleration/deceleration time and the residual vibration in a trapezoidal velocity profile is considered by analyzing the scenario when the jerking motion characteristic of such vibrations occurs. The results of experiments using a commercial robot show that residual vibrations can be reduced by the proposed method without the need for an additional feedback control algorithm while transferring a flexible object over small and large distances.

(1) Background: Conventional shoe manufacturing involves many processes that most workers avoid because of loud noises and harmful environments. Therefore, a robot-based shoe manufacturing system is needed to implement an automated process. (2) Aim: We propose a new robot-based shoe manufacturing automation system that includes an automatic robotic solution for replacing the manual manufacturing processes of the upper and sole. (3) Methods: For the upper manufacturing process, a new trajectory acquisition system with a digitizer and a shoe last turning device is proposed. A method to plan the robot’s tool path for roughing and cementing by industrial robot manipulators is also presented. For the sole manufacturing process, we adopted an industrial robot manipulator with a 3-D scanning system and a cementing tool. A trajectory generation algorithm for cementing the outer and inner sides of the sole by transforming 3-D information of the sole into a 6-D posture for the robot is proposed. (4) Results: All developed systems and proposed algorithms are applied to an automated production testbed, and their performances are experimentally verified. (5) Conclusions: The proposed system and methods can be applied for upper and sole manufacturing processes according to evaluation experiments in a demonstrative production line.

The demand for robots has increased in the industrial field, where robots are utilized in tasks that require them to move through complex paths. In the motion planning of a manipulator, path planning is carried out to determine a series of the positions of robot end effectors without collision. Therefore, it is necessary to carry out trajectory planning to determine position, velocity, and acceleration over time and to control an actual industrial manipulator. Although several methods have already been introduced for point-to-point trajectory planning, a trajectory plan which moves through multiple knots is required to allow robots to adapt to more complicated tasks. In this study, a trajectory planning based on the Catmull–Rom spline is proposed to allow a robot to move via several points in a task space. A method is presented to assign intermediate velocities and time to satisfy the velocity conditions of initial and final knots. To optimize the motion of the robot, a time-scaling method is presented to minimize the margin between the physical maximum values of velocity and acceleration in real robots and the planned trajectory, respectively. A simulation is then performed to verify that the proposed method can plan the trajectory for moving multiple knots without stopping, and also to check the effects of control parameters. The results obtained show that the proposed methods are applicable to trajectory planning and require less computation compared with the cubic spline method. Furthermore, the robot follows the planned trajectory, and its motion does not exceed the maximum values of velocity and acceleration. An experiment is also executed to prove that the proposed method can be applied to real robotic tasks to dispense glue onto the sole in the shoe manufacturing process. The results from this experiment show that the robot can follow the 3-D curved contour in uniform speed using the proposed method.

Autonomous car racing competitions have become widespread across many countries and events, taking inspiration from renowned motorsports like Formula 1 and IndyCar, among others. Similar to how traditional motorsports have fueled advancements in automotive technology, researchers and students are now anticipating similar contributions from autonomous car racing competitions. Betz et al. have provided a comprehensive review of the current landscape of autonomous car racing in [1] . These competitions feature a diverse range of robot cars, varying in scale from 1:10 to full-scale (refer to Table 1 for specifics). The size differences also extend to the sensory and computing systems employed. While many of these events are tailored for educational purposes, targeting students from high school to postgraduate programs, it is noted that smaller-scale hardware systems may not necessarily utilize the same sensory and computing systems as their real-scale counterparts. This, however, does not imply that the technical challenges in algorithm and program development are any less demanding than those encountered in full-scale or near full-scale cases. The variations in sensory, computing, and hardware resources due to the size differences may give rise to distinct technical problems not encountered by autonomous cars navigating real-world road environments. Nevertheless, fundamental issues, such as planning, motion control, and real-time recognition with onboard systems are common for both scales.

With depth cameras and LiDAR improving and generating more data, their applications in 3D point clouds are growing rapidly. However, the vast amount of generated data increases the computational load and results in a shortage of storage space. Therefore, a preprocessing step to reduce the number of points is required before using the 3D point cloud. This study proposes region of interest (ROI) determination methods that sequentially construct circular and rectangular ROIs along the target trajectory of the robot to extract only crucial data for the target task. These two ROI determination methods have two benefits. First, they maintain the resolution of the raw data; second, they create two ROIs that match perfectly regardless of the complexity of the trajectory. To verify the high performance of these two ROI determination methods, we conducted simulations and experiments using various data; the artificial frames, keyframes, and sequential frames. As a result, when the distance between the center points was small, 25% of the diameter or height of the circular and rectangular ROIs, the classification evaluation results were closer to 1 and the processing speed was faster than the raw data acquisition rate. However, we confirm that there is a trade-off relationship between the classification results and the processing time according to the distance parameter. In addition, through the qualitative comparison with the previous study, the long cuboid ROI determination method, we identified the limitations of the previous study and the advantages of the two proposed ROI determination methods.