The increasing adoption of industrial robot arms in advanced manufacturing has heightened the need for flexible trajectory planning methods that go beyond traditional offline programming (OLP) tools, which are often expensive, proprietary, and limiting. This study introduces an OLP-free pipeline designed to generate robot trajectory data and optimize paths for six-degree-of-freedom (6-DOF) robot arms using discrete reinforcement learning. Initially, five-axis NC code derived from CAD/CAM data is transformed into tool center point (TCP) trajectories through coordinate transformations. An analytical inverse kinematics solver then produces multiple joint solutions for each TCP pose, creating a discrete action space from which the learning agent can select feasible joint configurations along the trajectory. A reward function that considers variations in joint velocity and acceleration, as well as pose error, facilitates the simultaneous optimization of motion smoothness and tracking accuracy. The optimized trajectories are validated using an open-source physics simulator, showing enhanced motion stability, accuracy, and collision safety compared to conventional OLP-based paths. This proposed framework provides a flexible and cost-effective alternative to commercial OLP tools and lays a scalable foundation for future applications in automated and collaborative manufacturing systems.

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.

This study introduces an automated robotic system designed to replace manual maintenance in cold rolling mills, where hazardous confined spaces present significant safety risks to workers. To enhance safety and efficiency, we modified a commercial aerial work platform into a teleoperated mobile robot. The system includes a redesigned end-effector equipped with high-pressure cleaning nozzles and a wide-angle camera for visual inspection. Experimental validation in both laboratory and field settings demonstrated the system's maneuverability and effectiveness. The results indicate that this robotic solution can successfully reduce safety hazards by minimizing manual intervention while ensuring high-quality cleaning and inspection in industrial rolling mills.

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 paper presents an advanced robotic automation framework that combines an impedance-based compliance controller with an imitation learning network for high-precision peg-in-hole assembly. The framework is characterized by three key features. First, it employs an impedance-based compliance controller to ensure stable contact. This approach enables the robot to adapt flexibly to external contact forces, functioning like a spring-damper system to prevent potential damage. Second, domain randomization is applied to both geometric and visual properties within a high-fidelity simulation environment. This strategy effectively narrows the reality gap, enhancing robustness against environmental uncertainties and visual disturbances. Third, the framework utilizes an action-chunking-transformer (ACT) network to predict precise action sequences based on multimodal data, reducing compounding errors in trajectory generation and improving assembly success rates. Each feature is supported by specific advancements, such as real-time force feedback integration, diverse simulation scenario generation, and multimodal sensor fusion. Extensive experiments conducted in various unseen environments demonstrate the framework's effectiveness, confirming its suitability for complex assembly tasks that require high adaptability and precision under diverse conditions.

Multi-Agent Path Finding (MAPF) is an algorithm designed to identify collision-free paths for multiple agents, commonly used in fields like robotics and drone navigation. Conflict-Based Search with Continuous Time (CCBS) is particularly beneficial for real-world applications due to its capability to find paths in continuous time; however, it often experiences lengthy computation times. Although techniques such as prioritizing conflicts (PC), disjoint splitting (DS), and high-level heuristics have been implemented to reduce these times, challenges remain. To address these issues, this paper introduces methods to improve space utilization by calculating agent congestion. By optimizing space usage, we can identify paths that avoid potential collisions, even when those paths share the same cost. We propose enhancements to high-level heuristics, conflict prioritization, and low-level heuristics, as well as a method for calculating congestion in continuous time. These improvements lead to a reduction in agent collisions and a decrease in high-level expansions, resulting in a 30% increase in computational success rates compared to the existing CCBS. Incorporating space utilization into the search process significantly enhances MAPF performance.

This paper presents a tiltable cable-suspended aerial manipulation (SAM) system designed to improve the utility of aerial manipulators in industrial settings. Although drone-robot arm systems have shown promise, suspended configurations encounter notable stability challenges, particularly during inclined operations. To tackle these challenges, we performed simulation-based analyses focusing on the system's kinematics, dynamic response, and thrust requirements under tilted conditions. We utilized Monte Carlo sampling and forward kinematics to assess the workspace and manipulability. The findings indicated that each propeller needs to generate over 32 N of thrust to maintain stable control. Additionally, simulation experiments showed that the system can uphold its attitude and execute end-effector motions effectively, even in the presence of disturbances. This study establishes a foundational verification step toward developing a physical SAM system capable of safe and robust operation in inclined scenarios.

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.

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 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 study introduces a straightforward and cost-effective method to enhance the positional accuracy of a 6-axis serial robot using a double ball-bar (DBB). Kinematic errors, a primary source of inaccuracies in offline programming, are estimated and calibrated through circular tests. The kinematics of the robot are modeled using the Denavit-Hartenberg (D-H) convention, and a mathematical relationship between radial deviation and kinematic errors is established. To avoid singularities, identifiable parameters are selected using singular value decomposition. The method involves three steps: measuring the tool center point (TCP) with the DBB, estimating key kinematic parameters, and verifying the calibration results. Redundant or less significant parameters are excluded to concentrate on the most impactful ones. During the process, the robot is commanded to trace a circular path while radial deviations are recorded. This data is then utilized to estimate and adjust the kinematic model. After recalculating and executing the circular path with the calibrated model, a notable reduction in deviation is achieved. This proposed approach requires no additional equipment and provides a quick, affordable solution for improving the accuracy of industrial robots while lowering maintenance costs.

Ceiling inspections present challenges due to limited accessibility and structural constraints. To ease the burden on security personnel, who would otherwise need to manually disassemble, inspect, and restore ceiling components, this study proposes a robotic system for detecting hazardous objects within ceiling environments. The proposed system features several key innovations: a hollow-structured track mechanism designed to reduce vibrations from jolting while traversing structural beams and to improve localization accuracy. We optimized the robot’s mass distribution and required drive torque through dynamic simulations to ensure stable mobility in confined ceiling spaces. For effective hazardous object detection, we developed a YOLOv8-Seg-based background learning algorithm that suppresses ceiling-structure patterns, allowing for the identification of unknown objects without prior class-specific training. Additionally, we introduced a frame-based filtering algorithm to enhance detection reliability by reducing false positives caused by motion blur during movement. The system's effectiveness was validated through experiments conducted in a ceiling-structured testbed, demonstrating its capability for accurate hazardous object detection under realistic operating conditions.