Magnetic gears transmit torque via non-contact electro-magnetic coupling, which eliminates mechanical contact and significantly reduces wear, backlash, and noise compared to traditional mechanical gears. These benefits make magnetic gears particularly appealing for high-precision, high-reliability applications. However, achieving both high torque density and high gear ratios necessitates an optimized structural design that promotes efficient magnetic flux distribution while minimizing leakage and saturation. This study focuses on a hollow-type magnetic gear for collaborative robots that offers a high gear ratio. It employs topology optimization in conjunction with finite element analysis (FEA) to enhance torque density and efficiency. Key design variables, such as the geometry of the ferromagnetic core and the arrangement of permanent magnets, were optimized to increase average torque and reduce torque ripple and electro-magnetic losses. A prototype based on the optimized model was fabricated, and its performance was validated using a conventional direct torque measurement system. Experimental results were compared with simulation predictions to evaluate accuracy and analyze loss characteristics. The findings demonstrate the effectiveness of the proposed optimization approach and provide practical guidelines for designing high-efficiency magnetic gears suitable for advanced drive systems, including electric mobility and renewable energy applications.

We present an automated incasing process designed to replace traditional manual packaging of dried seaweed. This system consists of three key components: a cage mechanism that compresses and transfers six bundles, a handling device for stacking the bundles, and a collaborative robot that performs the box incasing operation based on sensor input. The handling device utilizes pneumatic actuators and a wire-linked folding plate to minimize interference within the confined box space, while also allowing for adjustable dimensions to accommodate seasonal variations in bundle size. Field validation was carried out under continuous input conditions using a conveyor. The collaborative robot followed a predefined sequence triggered by a presence sensor, effectively grasping, stacking, compressing, and transferring bundles without causing product damage. Experimental results indicated that the system successfully incased 72 bundles per box with stable performance and reliable placement. These findings demonstrate the feasibility of replacing labor-intensive operations with collaborative robotic automation in seafood packaging, highlighting opportunities for enhanced consistency, ergonomics, and productivity.

This study presents a method for inspecting ship block wall painting using a cooperative robot. The robot used in this study is a representative example of a human-collaborative robot system. The end-effector of the robot is equipped with a depth camera, designed in an eye-in style. The camera is used to measure and evaluate the thickness of the paint applied to the iron plate, simulating the conditions of ship block wall painting. To improve the accuracy of the recognition, an object detection algorithm with rapid computation and high accuracy was utilized. The algorithm was used to identify and outline the paint areas using the Canny edge algorithm. The proposed method successfully demonstrated the precision of paint area recognition by clearly identifying the center point and outline of the areas. Comparing the paint thickness measurements with laser distance measurements confirmed the effectiveness of the proposed method.