Robotic navigation in complex high-dimensional environments faces great challenges, especially in achieving efficient exploration, collision-free trajectory planning, and robust performance under dynamic conditions. Traditional optimization-based methods often suffer from limited adaptability, premature convergence, or insufficient obstacle handling. To address these challenges, we propose a novel hybrid path planning framework that integrates the Ant Colony Optimizer (ACO), the Whale Optimizer (WOA), the Artificial Potential Field (APF), and a random jump mechanism. This hybrid integration is characterized by the complementary global exploration, local optimization, and smooth trajectory generation, and is supported by a relativistic potential model and quantum-inspired mutation. Experimental results in various unstructured scenarios show that our method achieves significant performance improvements over six commonly used benchmarks including genetic algorithms (GA), genetically engineered word optimization (GWO), and quantum-inspired mutation (IRRT), with a 28.3% reduction in collision frequency and a 19.6% improvement in average path smoothness after preliminary simple quantification. In addition, our algorithm achieves the highest average navigation speed while maintaining the lowest variance, indicating that planning is consistent and efficient. These advantages make this method particularly suitable for practical robotics applications involving human passengers and dynamic obstacle environments.

Predicting the remaining useful life (RUL) of aircraft engines is crucial for ensuring flight safety, optimizing maintenance, and reducing operational costs. This paper introduces a novel hybrid deep learning model, Transformer–KAN–BiLSTM, for aero-engine RUL prediction. The model is designed to leverage the complementary strengths of its components: the Transformer architecture effectively captures long-range temporal dependencies in sensor data, the emerging Kolmogorov–Arnold Network (KAN) provides superior approximation flexibility and a unique degree of interpretability through its spline-based activation functions, and the Bidirectional LSTM (BiLSTM) extracts nuanced local temporal patterns. Evaluated on the benchmark NASA C-MAPSS dataset, the proposed fusion framework demonstrates exceptional performance, achieving remarkably low Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values that significantly surpass existing benchmarks. These results validate the model’s robustness and its high potential for practical deployment in prognostics and health management systems.

Aero-engines, as complex systems integrating numerous rotating components and accessory equipment, operate under harsh and demanding conditions. Prolonged use often leads to frequent mechanical wear and surface defects on accessory parts, which significantly compromise the engine’s normal and stable performance. Therefore, accurately and rigorously identifying failure modes is of critical importance. In this study, failure modes are categorized into notches, scuffs, and scratches based on original bearing structure images. The YOLOv8 architecture is adopted as the base framework, and a Dilated Reparameterization Block (DRB) is introduced to enhance the Dilation-Wise Residual (DWR) module. This structure uses a large convolutional kernel to capture fragmented and sparse features in wear images, ensuring a wide receptive field. The concept of structural reparameterization is incorporated into DWR to improve its ability to capture detailed target information. Additionally, the standard convolutional layer in the head of the improved DWR-DRB structure is replaced by Spatial-Depth Convolution (SPD-Conv) to reduce the loss of wear morphology and enhance the accuracy of fault feature extraction. Finally, a fusion structure combining Focaler and MPDIoU is integrated into the loss function to leverage their strengths in handling imbalanced classification and bounding box geometric regression. The proposed method achieves effective recognition and diagnosis of mechanical wear fault patterns. The experimental results demonstrate that, compared to the baseline YOLOv8, the proposed method improves the mAP50 for fault diagnosis and recognition from 85.4% to 91%.

Under laboratory conditions for recording a small amount of data, the characteristics of the phenomena distribution become a limitation of machine learning and advanced deep learning concepts for the diagnosis and localization of mechanical wear faults. In this paper, we adopt the combination of the diffusion model and TTT (test-time training), based on the sample distribution of feature data under the laboratory conditions, and we use the pre-trained decoder to decode the data into a continuous potential representation of natural language for sampling, to achieve data regeneration. Subsequently, the TTT algorithm becomes a model with weights in the hidden state itself. The gradient step on the self-supervised loss is selected as the update rule, which is trained synchronously during the testing time, adhering to the concept of migration learning, to construct a high-dimensional mapping relationship between the feature parameters and the failure modes of the mechanical wear. The final validation results show that the diagnosis accuracy reaches more than 95% for six types of typical aero-engine mechanical wear faults.