The variable stiffness of robot joints plays an important role in improving the robot’s compliance, safety, and energy efficiency. In this paper, a novel type of variable stiffness joint based on a rack and pinion structure (VSJ-RP) is proposed. The structure and the variable stiffness principle of the joint are described in detail. The theoretical stiffness calculation and the dynamic model of the joint are established, and the correctness of the model is validated by simulation. The compliance, safety, energy storage, and release characteristics of the joint are validated by position, bearing capacity, hitting ball, and safety detection experiments, respectively. These experimental results show that the joint stiffness can be adjusted from 14.74 Nm/rad to 726.58 Nm/rad, and the overshoot of the position response is about 5.56–0.5%. The larger the stiffness of the joint, the faster the adjustment response, the smaller the fluctuation, and the more stable the operation are. The maximum output torque of the joint is about 20 Nm, and the torque difference between the minimum and the maximum stiffness of the joint is about 10%. The energy conversion efficiency of the joint is 17.56%~89.86%, and the deformation angle range is 2.66°~4.37°. These phenomena reflect the safety, energy storage, and release capacity of the joint. An effective exploration is performed regarding the miniaturization, safety, and energy utilization of robot variable stiffness joints.

Detecting surface defects in industrial production presents challenges, including deploying lightweight algorithms on edge devices and balance between detection speed and accuracy. This paper introduces a novel lightweight method YOLO-FP for real-time detection of wire rope defects in industrial environments. Utilizing the YOLOv7tiny framework, we developed a lightweight cross-stage feature fusion module to enhance processing feature information. This mitigates the impact of redundant information from traditional convolution, reducing network size and improving detection speed. Additionally, the YOLO-FP network integrates lightweight convolution modules and an attention mechanism. Trained and tested on data from Changan, Great Wall, and Guangzhou Automobile models, our method achieved a 96.06% mean average precision, surpassing the original YOLOv7tiny model. Furthermore, it reduces the model size by 41.09% and enhances detection speed by 18.53%, making it promising for real-time edge device applications in wire rope production.

Title: New Method of Path Optimization for Medical Logistics Robots | Keywords: medical logistics robot, multiple steps traveling salesman problem model, improved ant colony optimization (IACO), refilling model | Author: Hui Jin, Qingsong He, Miao He, Fangchao Hu, and Shiqing Lu

In this paper, a novel special structure derived from the double-slider mechanism is presented and apply it to the design of a parallel mechanism, endowing the designed parallel mechanism with reconfigurable characteristics. First, a comprehensive analysis of the motion modes of the double-slider mechanism is carried out. By ingeniously varying the slider structure of the double-slider mechanism, a special structure capable of three distinct motion modes is obtained. This special structure is then integrated into the 3UPU parallel mechanism. As a result, the redesigned 3UPU parallel mechanism exhibits reconfigurability and can seamlessly switch among the three motion modes. Subsequently, the inverse kinematics, workspace, and singular pose of the parallel mechanism in these three modes are meticulously analyzed. Moreover, the Jacobian matrix is utilized to evaluate its flexibility and load-bearing performance. The analysis reveals that in different motion modes, all performance indicators of the mechanism are remarkable, indicating a promising application prospect. Finally, a prototype is fabricated using 3D printing technology to further validate the effectiveness of the proposed special structure. Additionally, its versatility is further explored and analyzed in-depth.

A method for measuring multi-joint finger trajectories is proposed using MediaPipe. In this method, a high-speed camera is used to record finger movements. Subsequently, the recorded finger movement data are input into MediaPipe, where the system automatically extracts the coordinate data of the key points in the finger movements. From this, we obtain data pertaining to the trajectory of the finger movements. In order to verify the accuracy and effectiveness of this experimental method, we compared it with the DH method and the Artificial keypoint alignment method in terms of metrics such as MAPE (Mean Absolute Percentage Error), maximum distance error, and the time taken to process 500 images. The results demonstrated that our method can detect multiple finger joints in a natural, efficient, and accurate manner. Then, we measured posture for three selected hand movements. We determined the position coordinates of the joints and calculated the angular acceleration of the joint rotation. We observed that the angular acceleration can fluctuate significantly over a very short period of time (less than 100 ms), in some cases increasing to more than ten times the initial acceleration. This finding underscores the complexity of finger joint movements. This study can provide support and reference for the design of finger rehabilitation robots and dexterous hands.