The automation of organic compound synthesis is pivotal for expediting the development of such compounds. In addition, enhancing development efficiency can be achieved by incorporating autonomous functions alongside automation. To achieve this, we developed an autonomous synthesis robot that harnesses the power of artificial intelligence (AI) and robotic technology to establish optimal synthetic recipes. Given a target molecule, our AI initially plans synthetic pathways and defines reaction conditions. It then iteratively refines these plans using feedback from the experimental robot, gradually optimizing the recipe. The system performance was validated by successfully determining synthetic recipes for three organic compounds, yielding that conversion rates that outperform existing references. Notably, this autonomous system is designed around batch reactors, making it accessible and valuable to chemists in standard laboratory settings, thereby streamlining research endeavors.

Although Light Detection and Ranging (LiDAR) is a sensor type for autonomous vehicles, it is recognized as an essential tool in various fields, such as drones, Unmanned Surface Vehicles (USVs), Unmanned Ground Vehicles (UGVs), and surveillance facilities. This is because LiDAR has advantages including a high resolution, 360-degree sensing capabilities, and consistent performance during the day and at night. However, LiDAR has the fatal disadvantage of performing poorly in snow, rain, and fog. Therefore, studies are attempting to remove noise points caused by snow, rain, and fog to ensure that LiDAR performs well even under extreme weather conditions. These studies began in approximately 2020, coinciding with accelerated research on autonomous driving, and they are still being actively pursued today. In particular, with the development of artificial intelligence technology in addition to the existing conventional methods, various approaches are being developed, but papers that thoroughly analyze and organize this research have not yet been published. Accordingly, in this study, we aim to comprehensively review the studies that have attempted to remove LiDAR sensor noise under extreme weather conditions through various methods. This review discusses the latest LiDAR denoising algorithms, with a particular focus on techniques implemented under snowy conditions. These algorithms can be categorized into five types: distance-based, intensity-based, fusion-based, learning-based, and other methods. In addition, this paper covers extreme weather datasets and related perception studies.

This letter presents a design framework and novel control strategy for a compact coaxial magnetic-gear-based actuation module suitable for small-to-mid-sized mechanical and robotic applications. The proposed actuation module adopts a non-contact magnetic coupling mechanism to transmit rotational power with a predetermined gear ratio, in contrast to traditional mechanical gear-based transmissions. This approach offers several advantages such as enhanced backdrivability, hardware safety, and transparency when compared to conventional contact-based transmissions. Furthermore, the magnetic coupling effect provides a spring-like characteristic that can be utilized to implement a series elastic actuation enabling sensorless torque control. The design of the magnetic gear was optimized using a differential evolution method, and a dynamic model was formulated to specify its dynamic characteristics. Finally, a composite disturbance observer-based torque control algorithm was developed, which capitalizes on the features of the magnetic spring. The proposed control algorithm was validated through several experiments.

Studies on autonomous driving have started to focus on snowy environments, and studies to acquire data and remove noise and pixels caused by snowfall in such environments are in progress. However, research to determine the necessary weather information for the control of unmanned platforms by sensing the degree of snowfall in real time has not yet been conducted. Therefore, in this study, we attempted to determine snowfall information for autonomous driving control in snowy weather conditions. To this end, snowfall data were acquired by LiDAR sensors in various snowy areas in South Korea, Sweden, and Denmark. Snow, which was extracted using a snow removal filter (the LIOR filter that we previously developed), was newly classified and defined based on the extracted number of snow particles, the actual snowfall total, and the weather forecast at the time. Finally, we developed an algorithm that extracts only snow in real time and then provides snowfall information to an autonomous driving system. This algorithm is expected to have a similar effect to that of actual controllers in promoting driving safety in real-time weather conditions.

Over the past decades, significant efforts have been dedicated to the development of ideal actuators for robotic systems, focusing on both mechanisms and design aspects [1], and they are implemented as a form of electric motors and hydraulic/pneumatic actuators. In the case of electric motors, they have emerged as a widely adopted choice for actuating robotic systems due to their compactness, ease of implementation, excellent linearity, and high-control bandwidth. Consequently, they remain one of the favorable compromise solutions for general robotic applications, offering exceptional dynamic output and precise control [2], [3]. In addition, electric motors exhibit a higher energy per weight or volume density than that of the human musculoskeletal system [4], [5], making them apparently advantageous for robotic applications.

This paper presents the concept of a new rubber compression mechanism for rotary compact series elastic elements, which can be used in robotic joints in human–machine interaction devices. A compact elastic element is realized using rubber materials with a higher specific energy and energy density than steel or fiberglass, which are commonly used in commercial springs. To overcome the nonlinearity and hysteresis of the proposed compression mechanism, a mathematical Bouc–Wen model developed through parameter identification is proposed. This paper describes the working principle of the new elastic element and presents the modeling method. Experiments on the Bouc–Wen model conducted to demonstrate the suggested mechanism are reported, and the results show that the proposed elastic element can be used for robotic joints in human–machine interaction devices.