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Robotic arms play a crucial role in various industrial operations, such as sorting, assembly, handling and spraying. However, traditional robotic arm control algorithms often struggle to adapt when faced with the challenge of dynamic obstacles. This paper aims to propose a dynamic obstacle avoidance method based on reinforcement learning to address real-time processing of dynamic obstacles.

This paper introduces an innovative method that introduces a feature extraction network that integrates gating mechanisms on the basis of traditional reinforcement learning algorithms. Additionally, an adaptive dynamic reward mechanism is designed to optimize the obstacle avoidance strategy.

Validation through the CoppeliaSim simulation environment and on-site testing has demonstrated the method's capability to effectively evade randomly moving obstacles, with a significant improvement in the convergence speed compared to traditional algorithms.

The proposed dynamic obstacle avoidance method based on Reinforcement Learning not only accomplishes the task of dynamic obstacle avoidance efficiently but also offers a distinct advantage in terms of convergence speed. This approach provides a novel solution to the obstacle avoidance methods for robotic arms.

When handling small-sized shafts and holes, achieving optimal safety, size compatibility and shape adaptability using rigid grippers presents significant problems. Recent advancements have introduced soft end-effectors that offer enhanced safety and adaptability for gripping parts. However, these soft end-effectors often struggle to maintain the necessary gripping positional accuracy. The purpose of this paper is to design a soft end-effector specifically engineered to address these problems, combining precise gripping capabilities with improved safety, positional accuracy and adaptability to the size and shape of fragile, small-sized components.

A soft finger with multilayer decreasing drive air chambers is designed to achieve the finger bending increasing from the root to the tip of the finger to improve the flexibility of the fingertip. Additionally, a three-finger self-centering configuration is employed, coupled with an expandable structure to increase the gripping range. Furthermore, a theoretical mathematical model of the finger is established. The physical prototype is manufactured and subjected to experimental testing, including gripping tests on small-sized, fragile shaft holes, to validate its operational performance.

The grasping experiments confirm that the designed end-effector can maintain coaxial positioning and meet adaptability requirements when handling fragile components with small-sized shaft holes. Furthermore, the addition of expanding palm structure increases the gripping attitude and enriches the application scene and gripping space.

The design of multilayer decreasing air chamber structure to solve the problem of poor gripping stability and low positional accuracy of soft manipulator; the expandable palm design is introduced to enhance gripping space; and solved the problem of gripping accuracy in the assembly of fragile parts with small-size shafts and holes.

Meteorological disasters pose a significant risk to people’s lives and safety, and accurate prediction of weather-related disaster losses is crucial for bolstering disaster prevention and mitigation capabilities and for addressing the challenges posed by climate change. Based on the uncertainty of meteorological disaster sequences, the damping accumulated autoregressive GM(1,1) model (DAARGM(1,1)) is proposed.

Firstly, the autoregressive terms of system characteristics are added to the damping-accumulated GM(1,1) model, and the partial autocorrelation function (PACF) is used to determine the order of the autoregressive terms. In addition, the optimal damping parameters are determined by the optimization algorithm.

The properties of the model were analyzed in terms of the stability of the model solution and the error of the restored value. By fitting and predicting the losses affected by meteorological disasters and comparing them with the results of four other grey models, the validity of the new model in fitting and prediction was verified.

The dynamic damping trend factor is introduced into the grey generation operator so that the grey model can flexibly adjust the accumulative order of the sequence. On the basis of the damping accumulated grey model, the autoregressive term of the system characteristics is introduced to take into account the influence of the previous data, which is more descriptive of the development trend of the time series itself and increases the effectiveness of the model.