In this paper, we propose a novel trajectory generation method for autonomous excavator teach-and-plan applications. Rather than controlling the excavator to precisely follow the teaching path, the proposed method transforms the arbitrary slow and jerky trajectory of human excavation into a topologically equivalent path that is guaranteed to be fast, smooth and dynamically feasible. This method optimizes trajectories in both time and jerk aspects. A spline is used to connect these waypoints, which are topologically equivalent to the human teaching path. Then the trajectory is reparametrized to obtain the minimum time-jerk trajectory with the kinodynamic constraints. The optimal time-jerk trajectory generation method is both formulated using nonlinear programming and conducted iteratively. The framework proposed in this paper was integrated into a complete autonomous excavation platform and was validated to achieve aggressive excavation in a field environment.

In practical engineering scenarios, machines are seldom in a faulty operating state, so it is difficult to get enough available sample data to train the fault diagnosis model, leading to the problem of the small and unbalanced number of rotating machinery fault samples and low fault diagnosis accuracy. To solve this problem, this paper introduces a novel approach to machinery fault diagnosis. This approach involves the integration of a Convolutional Attention Residual Network (CBAM-ResNet) with a Graph Convolutional Neural Network (GCN). Firstly, to comprehensively exploit time-domain information from one-dimensional vibration signals, this study utilize Gram Angular Field (GAF) coding to transform traits of vibration signals into two-dimensional image characteristics. The resultant two-dimensional image is then expanded by applying the Wasserstein Distance Gradient Penalty Generation Adversarial Network (WGAN-GP) to produce a representative sample image. Secondly, the image is input to CBAM-ResNet to perform focused feature extraction and construct the feature matrix. Lastly, the adjacency matrix is derived through Graph Generation Layer (GGL); subsequently, the feature matrix and adjacency matrix are utilized as inputs for the GCN. After deep feature extraction, fault feature classification is executed via Softmax. Performance tests were conducted using the Case Western Reserve University bearing dataset and the planetary gearbox dataset. The method demonstrated remarkable results, achieving an accuracy of over 99% on the unbalanced dataset and surpassing 98% in 0dB noise compared to various other models. This illustrates the effectiveness and feasibility of the proposed method.

Researchers borrow ideas from biological characteristics and behavior in design to make bionic robots that can meet unstructured and complex operating environments. The elephant trunk has been widely imitated by bionic robots because of its strong dexterity and stiffness adjustability. Due to the complex structure of the current elephant trunk robot, a series-parallel elephant trunk robot based on flexible rod actuation and a 6-degree-of-freedom (6-dof) parallel module is proposed in this paper. The bionic robot has a simple structure and redundant kinematics, which can realize the control of stiffness. This work focuses on the modeling of the flexible driving rod, the kinematics of a single parallel module, and the whole biomimetic robot. The kinematics are verified by simulation, which lays a foundation for future research on stiffness regulation.