In recent years, the increasing number of patients with spinal cord injuries, strokes, and lower limb disabilities has led to the gradual development of rehabilitation-assisted exoskeleton robots. A critical aspect of these robots is their ability to accurately sense human movement intentions to achieve smooth and natural control. This paper describes research carried out on predicting the motion angles of human lower limb joints. Based on the design of a signal acquisition system for physiological muscle signals and inertial measurement unit (IMU) data, a hybrid neural network prediction model (QRTCN-BiLSTM) and a single neural network prediction model (QRBiLSTM) were constructed using quantile regression, temporal convolution network (TCN) and bidirectional long short-term memory network (BiLSTM), respectively. At the same time, 7-channel surface electromyographic signals (sEMG) and 12-channel IMU data from hip and knee joints were collected and input into the QRBiLSTM and QRTCN-BiLSTM models to unfold the training and analyze the comparison. The results show that the QRTCN-BiLSTM model can more accurately infer human movement intention and provide a more reliable and accurate prediction tool for human–robot interaction research in rehabilitation robotics.

In this paper, an improved lightweight YOLOv8 method is proposed to detect the ripeness of tomato fruits, given the problems of subtle differences between neighboring stages of ripening and mutual occlusion of branches, leaves, and fruits. The method replaces the backbone network of the original YOLOv8 with a more lightweight MobileNetV3 structure to reduce the number of parameters of the model; at the same time, it integrates the convolutional attention mechanism module (CBAM) in the feature extraction network, which enhances the network's capability of extracting features of tomato fruits. At the same time, it introduces the SCYLLA-IoU (SIoU) as a bounded YOLOv8 frame regression loss function, effectively solving the mismatch problem between the predicted frame and the actual frame and improving recognition accuracy. Compared with the current mainstream models Resnet50, VGG16, YOLOv3, YOLOv5, YOLOv7, etc., the model is in an advantageous position regarding precision rate, recall rate, and detection accuracy. The research and experimental results show that the mean values of precision, recall rate, and average precision of the improved MCS-YOLOv8 model under the test set are 91.2%, 90.2%, and 90.3%, respectively. The detection speed of a single image is 5.4ms, and the model occupies less memory by 8.7 M. The model has a clear advantage in both detection speed and precision rate and also shows that the improved MCS-YOLOv8 model can provide strong technical support for tomato-picking robots in complex environments in the field.

Rigid–flexible coupled robots have problems such as vibration and elastic deformation caused by the flexibility of the members during the motion process, which significantly impacts the system’s motion accuracy and dynamics performance. To address the above problems, a dynamic modeling method based on a vector bond graph is proposed, and a multi-energy domain global dynamic model of Delta-type rigid–flexible coupled parallel robot considering rod flexibility is established. The coupled vibration of the control part and mechanical part of the system is analyzed, and model simulation is verified by 20-sim 4.4 software and ADAMS software (instructional version), which verifies the validity and reasonableness of the modeling method and provides a reference for the modeling of other rigid–flexible coupled systems with parallel systems in space.

To address the challenge of predicting lubrication states and reliability caused by the uncertainty of gear materials and structural parameters, a lubrication reliability analysis method considering the randomness of gear parameters is proposed. Firstly, a nonlinear dynamic model of a gear pair is established to derive the dynamic meshing force. The geometric and kinematic analyses are then performed to determine time-varying equivalent curvature radius and entrainment velocity. The minimum film thickness during meshing is further calculated. Considering gear parameters as random variables, a gear lubrication reliability model is formulated. Monte Carlo Simulation method is employed to accurately analyze the dynamic response, dynamic meshing force, equivalent curvature radius, entrainment velocity, probability distribution of minimum film thickness, and gear lubrication failure probability. Additionally, a specialized wear test device is designed to investigate the evolution of tooth surface roughness with wear and to forecast trends in gear lubrication failure probability as wear progresses. The results indicate that the uncertainty in gear parameters have minimal impact on the equivalent curvature radius and entrainment velocity, but significantly affect the dynamic meshing force. The gear speed and root mean square roughness are critical factors affecting lubrication reliability, and the early wear of the teeth enhances the lubrication reliability. The present work provides valuable insights for the design, maintenance, and optimization of high-performance gear systems in practical engineering applications.