The rising demand for robots in warehouses has highlighted the need for efficient multi-robot algorithms. In response, researchers have focused on Multi-Agent Path Finding (MAPF), which enables multiple agents to calculate conflict-free paths to their individual goals. However, the computation time of conflict-based MAPF algorithms significantly increases as the number of conflicts rises, a common challenge in warehouse environments with narrow passages or corridors. To tackle this issue, this study introduces a new type of conflict called “Overlap Conflict.” Overlap Conflicts occur when an agent stops, causing chain conflicts among subsequent agents traveling in the same direction. When an Overlap Conflict arises, the affected agents are dynamically merged into a single group, shifting the conflicts from an individual level to a group level. If the merged agents find themselves with unreachable goals, they are split back into individual agents to continue calculating paths to their respective destinations. This approach effectively reduces computation time in congested environments, particularly in narrow corridors where alternative routes exist.

This paper presents the design of an automatic circumferential chamfering device that processes the inner and outer diameter corners of centrifugal cast pipes after cutting. These large, heavy pipes (dimensions: 389 mm x 5700 mm x 36 mm; weight: 1,200 kg) are produced using the centrifugal casting method. Following manufacturing, the pipes undergo several post-processing operations, including washing, grinding, cutting, and chamfering. Traditionally, circumferential chamfering has been performed manually by workers using grinders. In this study, we conceptualized an automatic circumferential chamfering device specifically designed to chamfer the corners of large centrifugal cast pipes. A structural analysis was conducted to ensure the design's safety, yielding a safety factor greater than two. Based on these design outcomes, we manufactured the chamfering device and conducted characteristic experiments on a large centrifugal cast pipe. The results confirmed that the cylindrical chamfering device can safely and effectively chamfer the inner and outer diameters of large centrifugal cast pipes.

This paper presents model-based hysteresis and cross-coupling compensators designed for precise control of a piezoelectric fast steering mirror (FSM). The hysteresis compensators are developed by inversely modeling the variation in the force constant relative to various excitation voltages, enabling the system to maintain linear response characteristics across a broad range of input amplitudes. The cross-coupling compensator is formulated by creating a decoupling matrix that cancels out coupling effects, generating signals of equal magnitude and opposite phase for each axis. The implementation of these compensators reduces the hysteresis band and magnitude uncertainty in the FSM dynamics by over 89.6% and 74.2%, respectively, while also significantly suppressing cross-coupling effects by more than 85.5%. Furthermore, the performance of the proposed compensators is validated in a closed-loop control system, demonstrating a notable reduction in cross-axis vibrations and improved tracking performance in response to step reference inputs and highfrequency sinusoidal trajectories.

This study introduces a novel adjustable fastening mechanism for wearable robots, aimed at alleviating user discomfort associated with traditional fixed attachment methods. By utilizing the unique scissoring effect of braided sleeves, we demonstrated that axial manipulation can effectively translate into radial size control, allowing for precise regulation of fastening force. To address the size limitations of commercial braided sleeves, we developed a large-area fastening structure by combining multiple braided sleeve sheets. Additionally, we incorporated a wire tendon system to enable active operation in both Daily Mode (fastening-release) and Exercise Mode (fastening-tightening). Experimental results on an anthropomorphic model revealed that this adjustable fastening structure offers variable fastening forces, achieving a 4.8-fold difference between the exercise and daily modes. This research presents a new approach by leveraging the Poisson's ratio properties of braided sleeves for dynamic fastening, tackling fabrication challenges for large-area structures, and improving user comfort and compliance in wearable robot applications

The Split Hopkinson Pressure Bar (SHPB) experiment is commonly employed to assess the dynamic mechanical properties of materials under high strain-rate conditions (10²-10⁴ s-¹) through the propagation of elastic stress waves via pressure bars. The precision and dependability of SHPB measurements are heavily influenced by the alignment of the specimen with the bars. Misalignment can lead to flexural vibrations, causing waveform distortion and undermining the assumption of onedimensional stress waves. While previous research has explored the impact of misalignment on waveform characteristics, pinpointing the specific sources of distortion from measured signals remains a challenge. This study introduces a machine learning-based classification method that extracts features from distorted SHPB waveforms to identify the type of misalignment. Incident wave signals under various misalignment scenarios were simulated using the commercial finite element software LS-DYNA, and the extracted features were utilized to create a training dataset. Several machine learning models, including XGBoost, were trained and evaluated, with XGBoost yielding the highest accuracy and F1-score. The trained model was then applied to experimentally measured distorted waveforms to validate its effectiveness. This proposed approach facilitates the automated diagnosis of distortion sources in SHPB data, reducing the need for manual interpretation and improving analysis efficiency.

This study details the development of an ultra-precision air-bearing stage that integrates real-time motion error measurement and compensation features. The motion errors addressed include horizontal and vertical straightness errors, as well as roll, pitch, and yaw errors. These errors are measured by an embedded system that incorporates five capacitive sensors and a reference mirror within the stage. A key advantage of this stage is its capability to perform real-time compensation using the internal measurement system and on-stage pneumatic regulators, eliminating the need for external measurement and compensation devices. Experimental results show a significant reduction in motion errors, with horizontal and vertical straightness errors decreasing from 3.09 and 1.95 μm to 0.29 and 0.25 μm, respectively. Additionally, roll, pitch, and yaw errors were reduced from 3.18, 3.45, and 4.93 arcsec to 0.35, 0.41, and 0.49 arcsec, respectively. These results clearly demonstrate the effectiveness of the proposed approach.

In this study, we demonstrate a well-established strategy for controlling the threshold voltage (Vth) in organic thin-film transistors (OTFTs) by applying uniform gold nanoparticle (AuNP) coatings onto silver nanowire (AgNW) electrodes using a galvanic replacement process in the presence of NaCl. This approach highlights the potential for low-energy consumption operation. The AuNP coatings effectively adjust the work function of the AgNW electrodes to better match that of the organic semiconductor. As a result, the OTFT devices show significantly reduced threshold voltages, enhancing charge injection efficiency and lowering the operating voltage. Additionally, when used as synaptic transistors, the optimized Aucoated AgNW composite electrodes demonstrate superior neuromorphic performance, including a lower maximum drain voltage (VDS), indicating a potential for improved energy efficiency per spike event. This advancement marks a critical step toward developing low-power neuromorphic devices and low-voltage flexible electronics. Our work establishes a practical methodology for quantitatively and reproducibly controlling Vth through precise modulation of metal coating uniformity, providing a solid technological foundation for future optimization of organic electronic devices.

This paper details the design and development of a robotic joint actuator that combines a frameless BLDC motor with a two-stage stepped planetary gear reducer, as well as a custom-built controller for precise position control. The rotor is physically coupled to a hollow sun gear shaft to facilitate internal cable routing, and the actuator features a high-resolution absolute encoder utilizing the BiSS-C protocol. The controller includes a 3-phase H-bridge driver, differential signal conversion for encoder communication, and a CAN interface for host communication. Position control is achieved through a PID loop operating at 1 kHz. A prototype actuator and controller have been fabricated, and step response tests were conducted. Experimental results indicate stable and accurate tracking of position commands, with a short settling time of 0.04773 seconds. These findings confirm the effectiveness of the integrated actuator system for robotic joint applications. Future work will focus on optimizing internal cable space and implementing sensorless control algorithms.

The future mobility industry is increasingly utilizing advanced tools for cutting and machining lightweight parts to enhance the fuel efficiency of automotive engines. Machining companies are turning to polycrystalline diamond (PCD) tools to boost productivity in the production of these lightweight components. PCD tools provide exceptional machining performance and a long service life, making them ideal for high-mix, low-volume production, which often involves customized requirements for various materials. To further improve efficiency, this study explores the application of metal 3D printing technology in the manufacturing of PCD tools. This technology allows for the creation of PCD tools with superior cutting performance and wear resistance, tailored for high-speed machining of lightweight materials, including complex shapes. Thus, research into this area is essential. In this study, we manufactured boring tools by brazing PCD tips onto three different laminated structures created using Fused Deposition Modeling (FDM), a method within metal 3D printing technologies. We then evaluated the fabricated boring tools through comparative machining experiments against existing sintered PCD boring tools. The results indicated that the 3D-printed solid tools demonstrated no significant differences in machining accuracy or surface quality compared to the conventional tools.

Carbon nanotubes (CNTs) are popular in strain sensors due to their exceptional electrical conductivity, flexibility, and sensitivity to deformation. In this study, a high-sensitivity strain sensor was fabricated by spray-coating CNT ink onto various paper substrates, with “lint-free paper” identified as the optimal choice. A total of 10 spray cycles ensured a reliable conductive coating. To enhance durability and broaden application potential, a PET protective layer was incorporated. The sensor's performance was assessed through bending tests using a push-pull gauge across a strain range of 0-2%. The lintfree paper-based sensor exhibited a consistent response up to 1.4% strain. The measured gauge factors (GF) were 121.370 in the 0-0.3% range, 70.999 in the 0.3-0.8% range, and 20.935 in the 0.8-1.4% range. A precise response was also noted when adjusting the bending angle in 1° increments, particularly within the 0-20° range. Additionally, the sensor was tested on the human wrist, confirming its viability for wearable applications. These findings indicate that the lint-free paper-based CNT strain sensor offers high sensitivity and measurement precision within narrow strain ranges. Its lightweight structure and flexible design suggest strong potential for practical use in areas such as sports monitoring and human motion detection.

Cable chains are essential in the semiconductor industry for preventing the twisting or sagging of moving cables. They can be broadly categorized into two types based on their fastening methods, with rivet-based assembly being the most common. An alternative method utilizes integral locking features without rivets, which simplifies manufacturing and reduces production costs. However, integral cable chains are more susceptible to breakage during assembly, limiting their use in various industrial environments.This study introduces a structural design approach aimed at minimizing localized stress during assembly while ensuring the cable chain meets the required retention force. Design variables were selected from the modifiable features of the integral cable chain. Through sensitivity analysis, we identified key variables that significantly influence the retention force, which allowed us to reduce the number of design iterations. By employing finite element analysis and response surface methodology, we derived an optimal shape that achieved the target pull-out force and resulted in a 9.7% reduction in assembly stress compared to the original design.

Robots are increasingly utilized in manufacturing and logistics, where bin-picking has become crucial for managing randomly placed objects. However, traditional methods often rely on expensive 3D vision systems, have limited adaptability to unstructured environments, and primarily focus on the picking process, neglecting the placing tasks. To address these challenges, this study presents a cost-effective system that combines a depth camera, YOLO-based instance segmentation, and optimization-based inverse kinematics for real-time object detection and stable manipulation. In the placing stage, an adaptive algorithm detects empty tray holes and generates grid patterns, ensuring reliable placement even in the presence of tray misalignments, occupied slots, or partial occlusions. Experimental validation revealed a 91% success rate in mixed-object environments during picking tasks and a 94% success rate for placing tasks, even with tray displacement and occlusion conditions. The results demonstrate that the system maintains stable performance across both picking and placing processes while minimizing reliance on expensive hardware and complex initial setups. By enhancing flexibility and scalability, the proposed approach offers a practical solution for intelligent automation and can serve as a foundation for broader applications in assembly, logistics, and service robotics.