Since fast and accurate motion profiles are directly related to the productivity of manufacturing, many studies have been performed to optimize such motion profiles. However, previous research has not provided an appropriate solution regarding which motion profile is the best for a given situation. Therefore, in this article, we propose an optimization process for polynomial-function-based motion profiles to achieve fast movement with low vibration. The proposed process describes a method of optimizing an nth-order polynomial motion profile to achieve a fast transfer time and minimal residual vibration characteristics with extra robustness to system uncertainty for a given moving distance and given actuator capacities. Then, we describe how to determine the best order of the optimized motion profile for given conditions, such as the allowable computational load, the required residual vibration bound, and the required robustness against system uncertainties. The performance of the optimized motion profile is demonstrated, and the suggested process for obtaining the best order of the optimal motion profile is applied to a practical example of a wafer transfer robot.

Vehicles today have many advanced driver assistance control systems that improve vehicle safety and comfort. With the development of more sophisticated vehicle electronic control and autonomous driving technology, the need and effort to estimate road surface conditions is increasing. In this paper, a real-time road surface classification algorithm, based on a deep neural network, is developed using a database collected through an intelligent tire sensor system with a three-axis accelerometer installed inside the tire. Two representative types of network, fully connected neural network (FCNN) and convolutional neural network (CNN), are learned with each of the three-axis acceleration sensor signals, and their performances were compared to obtain an optimal learning network result. The learning results show that the road surface type can be classified in real-time with sufficient accuracy when the longitudinal and vertical axis acceleration signals are trained with the CNN. In order to improve classification accuracy, a CNN with multiple input that can simultaneously learn 2-axis or 3-axis acceleration signals is suggested. In addition, by analyzing how the accuracy of the network is affected by number of classes and length of input data, which is related to delay of classification, the appropriate network can be selected according to the application. The proposed real-time road surface classification algorithm is expected to be utilized with various vehicle electronic control systems and makes a contribution to improving vehicle performance.

Although the LiDAR sensor provides high-resolution point cloud data, its performance degrades when exposed to dust environments, which may cause a failure in perception for robotics applications. To address this issue, our study designed an intensity-based filter that can remove dust particles from LiDAR data in two steps. In the first step, it identifies potential points that are likely to be dust by using intensity information. The second step involves analyzing the point density around selected points and removing them if they do not meet the threshold criterion. To test the proposed filter, we collected experimental data sets under the existence of dust and manually labeled them. Using these data, the de-dusting performance of the designed filter was evaluated and compared to several types of conventional filters. The proposed filter outperforms the conventional ones in achieving the best performance with the highest F1 score and removing dust without sacrificing the original surrounding data.

We demonstrate that microlensing can be used for detecting planets in binary stellar systems. This is possible because in the geometry of planetary binary systems where the planet orbits one of the binary component and the other binary star is located at a large distance, both planet and secondary companion produce perturbations at a common region around the planet-hosting binary star and thus the signatures of both planet and binary companion can be detected in the light curves of high-magnification lensing events. We find that identifying planets in binary systems is optimized when the secondary is located in a certain range which depends on the type of the planet. The proposed method can detect planets with masses down to one tenth of the Jupiter mass in binaries with separations <~ 100 AU. These ranges of planet mass and binary separation are not covered by other methods and thus microlensing would be able to make the planetary binary sample richer.Comment: 5 pages, two figures in JPG forma

Dual crane lifting, wherein two cranes collaborate to lift a single workpiece, serves as an essential solution in scenarios in which employing a single, sufficiently large crane is impractical due to cost constraints, ground conditions, and spatial limitations. Due to the complexity of double crane lifting operations, the implementation of automated path generation minimizes the risk of human error and removes the potential for accidents by simulating and validating the generated crane path. We propose a novel multiple rapidly-exploring random trees (RRT) based algorithm designed specifically for dual crane systems to produce lifting paths, particularly in challenging ’narrow path finding’ scenarios. The multiple RRT method is an efficient way to find paths in environments with high complexity and low connectivity through a strategy that allows new trees to be generated and grown whenever a newly generated node that cannot be connected to an existing tree occurs. The proposed path planning algorithm not only adapts the multiple RRT method to the dual crane systems but also incorporates ideas to enhance the optimality of generated paths while reducing computational time. The effectiveness of this algorithm has been validated through a case studies covering various scenario.

3-D simulations for lunar sodium exosphereReproduction of lunar sodium brightness near the MoonReproduction of sodium brightness and velocity distribution at tail