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.

3D ground reaction force (GRF) estimation during walking is important for gait and inverse dynamics analyses. Recent studies have estimated 3D GRF based on kinematics measured from optical or inertial motion capture systems without force plate measurement. A neural network (NN) could be used to estimate ground reaction forces. The NN network approach based on segment kinematics requires the selection of optimal inputs, including kinematics type and segments. This study aimed to select optimal input kinematics for implementing an NN for each foot’s GRF estimation. A two-stage NN consisting of a temporal convolution network for gait phase detection and a gated recurrent unit network was developed for GRF estimation. To implement the NN, we conducted level/inclined walking and level running on a force-sensing treadmill, collecting datasets from seven male participants across eight experimental conditions. Results of the input selection process indicated that the center of mass acceleration among six kinematics types and trunk, pelvis, thighs, and shanks among 15 individual segments showed the highest correlations with GRFs. Among four segment combinations, the combination of trunk, thighs, and shanks demonstrated the best performance (root mean squared errors: 0.28, 0.16, and 1.15 N/kg for anterior-posterior, medial-lateral, and vertical components, respectively).

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.

Hybrid mobile robot is the system that will practically combine legged walking and skated driving in the same system. Therefore, this robot has own problems of inverse kinematics that are not considered in typical walking robots. In this paper, I fully categorized the inverse kinematics problems for hybrid mobile robot with general motion by walking and driving on an inclined plane, including switching end-effectors between foots and blades. I also solved the inverse kinematics for each case of problems. I here actively adopted the coordinate transformation derived from the inclined plane to cope with the random motion of foots and blades on the plane. I then presented several examples of the inverse kinematics problems with specific situations, and verified the validity of the analysis method from the results.

Water spraying work to prevent the dust from scattering during building dismantling operation has usually been done manually. Since it is very risky and often causes fatal accidents due to unexpected collapse, a few countries have adopted mechanical water spaying machines. However, these machines are still operated by human laborer, specifically in orienting the spraying direction, which induces low dust suppression efficiency. In this research, an automated fine dust tracking system was suggested to identify and track the dust generating position accurately. A GPS is installed on the secured body of the excavator which contains a crusher as an end-effector for building dismantlement. Assuming the position of the crusher is the dust generating spot, a forward kinematics analysis is performed to identify the crusher position from the body origin on which the GPS sensor is placed. With another GPS on the water-spraying robot, its relative position to the dust generating spot and its heading angle for tracking can be calculated consequently. Tracking experiments were conducted with a miniature excavator and a reduced size water spraying robot. The results showed a sufficient tracking performance enough to be applied to the water spaying machines.

UVW Stage is widely used in manufacturing processes of PCB, LCD, OLED, and semiconductor industries. The precision of UVW Stage is closely associated with the quality of products. Two approaches for kinematics of UVW Stage are proposed for comparative analysis. Program of proposed kinematics algorithm is developed for motion control and applied to UVW Stage driving. The position of the stage for each algorithm is sequentially measured by laser interferometer. Both virtual stage and real stage are used for accuracy analysis. The performance of each algorithm is evaluated based on this accuracy analysis.

The purpose of this study was to examine the effect of gender and foot landing type (forefoot vs. rearfoot landing) on kinematics, kinetics, and energy absorption of lower extremity joint. Twenty males and twenty females performed single-leg landing with two different foot landing types: forefoot landing and rearfoot landing. Three-dimensional kinematic and kinetic parameters were measured using motion capture system. Greater knee valgus angle at peak vertical ground reaction force (p = 0.034) during rearfoot landing increased the risk of anterior cruciate ligament (ACL) injury in females as increasing valgus positioning from neutral alignment could increase the load on ACL. Greater contribution of ankle joint and less contribution of hip joint in energy dissipation were found in females during both forefoot (p = 0.029 and p = 0.016, respectively) and rearfoot landing (p = 0.003 and p = 0.016, respectively). These results suggest that increasing muscular activity of ankle plantarflexor could reduce shock transmission to the proximal joint in females. In addition, greater hip joint’s contribution to total negative work in males induced lower hip flexion angle found in both forefoot and rearfoot landing by elevated activation of the hip extensor. In conclusion, landing strategy differs between genders in both forefoot and rearfoot landing.