With the rapid development of new-generation artificial intelligence and Internet of Things technology, mobile robot technology has been widely used in various fields. Among them, the autonomous path-planning technology of mobile robots is one of the cores for realizing their autonomous driving and obstacle avoidance. This study conducts an in-depth discussion on the real-time and dynamic obstacle avoidance capabilities of mobile robot path planning. First, we proposed a preprocessing method for obstacles in the grid map, focusing on the closed processing of the internal space of concave obstacles to ensure the feasibility of the path while effectively reducing the number of grid nodes searched by the A* algorithm, thereby improving path search efficiency. Secondly, in order to achieve static global path planning, this study adopts the A algorithm. However, in practice, algorithm A has problems such as a large number of node traversals, low search efficiency, redundant path nodes, and uneven turning angles. To solve these problems, we optimized the A* algorithm, focusing on optimizing the heuristic function and weight coefficient to reduce the number of node traversals and improve search efficiency. In addition, we use the Bezier curve method to smooth the path and remove redundant nodes, thereby reducing the turning angle. Then, in order to achieve dynamic local path planning, this study adopts the artificial potential field method. However, the artificial potential field method has the problems of unreachable target points and local minima. In order to solve these problems, we optimized the repulsion field so that the target point is at the lowest point of the global energy of the gravitational field and the repulsive field and eliminated the local optimal point. Finally, for the path-planning problem of mobile robots in dynamic environments, this study proposes a hybrid path-planning method based on a combination of the improved A* algorithm and the artificial potential field method. In this study, we not only focus on the efficiency of mobile robot path planning and real-time dynamic obstacle avoidance capabilities but also pay special attention to the symmetry of the final path. By introducing symmetry, we can more intuitively judge whether the path is close to the optimal state. Symmetry is an important criterion for us to evaluate the performance of the final path.

Soft robotics is a specialized field of robotics that focuses on the design, manufacture, and control of robots made of soft materials, as opposed to those made of rigid links. One of the primary challenges for the future use of continuous or hyper-redundant robotics systems in industrial and medical technology is the development of suitable modeling and control approaches. Due to the complex non-linear behavior of soft materials and the unpredictable motion of actuators, the task of modeling complex soft actuators is very time-consuming. As a result, earlier studies have undertaken research into model-free methods for controlling soft actuators. In recent years, machine learning (ML) methods have become widely popular in research. The adaptability of an ML model to a non-linear soft drive system alongside the varying actuation behavior of soft drives over time as a result of material characteristics and performance requirements is the key rationale for including an ML model. The system requires the online updating of the ML model in order to work with the non-linear system. Sequential data collected from the test bench and converted into a hypothesis are used to perform incremental learning. These methods are called lifelong learning and progressive learning. Real-time data flow training is combined with incremental learning (IL), and a neural network model is tuned sequentially for each data input. In this article, a method for the intelligent control of soft pneumatic actuators based on an incremental learning algorithm is proposed. A soft pneumatic actuator was subjected to three distinct test conditions in a controlled test environment for a specified duration of data gathering. Additionally, data were collected through finite element method simulations. The collected data were used to incrementally train a neural network, and the resulting model was analyzed for errors with both training and test data. The training and testing errors were compared for different incremental learning (IL) algorithms, including K-nearest neighbors, a decision tree, linear regression, and a neural network. The feasibility of the modulo-free intelligent control of soft pneumatic actuators based on an incremental learning algorithm was verified, solving the problem of the control of software actuators.

The paper presents a study of an intelligent system for personnel positioning, transport, and equipment monitoring in the mining industry using convolutional neural network (CNN) and OpenPose technology. The proposed framework operates through a three-stage pipeline: OpenPose-based skeleton extraction from surveillance video streams, capturing 18 key body joints at 30fps; multimodal feature fusion, combining skeletal key points and proximity sensor data to achieve environmental context awareness and obtain relevant feature values; and hierarchical pose alert, using attention-enhanced bidirectional LSTM (trained on 5000 annotated fall instances) for fall warning. The experiment conducted demonstrated that the combined use of the aforementioned technologies allows the system to determine the location and behavior of personnel, calculate the distance to hazardous areas in real time, and analyze personnel postures to identify possible risks such as falls or immobility. The system’s capacity to track the location of vehicles and equipment enhances operational efficiency, thereby mitigating the risk of accidents. Additionally, the system provides real-time alerts, identifying abnormal behavior, equipment malfunctions, and safety hazards, thus promoting enhanced mine management efficiency, improved safe working conditions, and a reduction in accidents.

Coal remains a vital energy resource and plays a key role in national development. Ensuring the safety of underground mining personnel is essential, and intelligent algorithms are increasingly used to detect miners in surveillance footage. However, complex underground environments—characterised by poor lighting, occlusions, irregular postures, and reflective gear—make accurate detection difficult. This study proposes improvements to the YOLOv10-N object detection model for miner detection. Using 37,463 annotated images from real mining environments, we propose three main enhancements: a Coordinate Attention (CA) mechanism to highlight important spatial features, a Dynamic Head (DyHead) module to improve multi-scale feature fusion, and the Efficient IoU (EIOU) loss function to enhance bounding box regression and speed up convergence. While CA, DyHead, and EIOU are established methods, their synergistic integration for asymmetric miner detection (e.g., occluded limbs, uneven lighting) presents a novel application-specific optimisation. Experimental results confirm that the enhanced model significantly outperforms the original. It achieves 92.69% accuracy, 87.53% recall, and an average accuracy of 89.9%, with a practical detection effect of 68.24%. These findings show that the proposed method improves both accuracy and robustness in challenging mining conditions while maintaining processing efficiency.

Nowadays, electric motors are an integral part of most modern electromechanical systems that are used in industry. It follows that industrial processes are becoming more dependent on their efficiency. If faults in electric motors are not rectified, they can lead to malfunctions and accidents, as well as production downtime. Symmetry of a three-phase system means that the voltage and current in the three phase conductors are equal to each other, with a period of 120°. Asymmetry occurs if one of these conditions or both conditions are violated at the same time. In most cases, asymmetry is caused by loads. Predictive diagnostics is the most effective way to identify motor faults while the motor is in operation and prevent the likelihood of failure. Predictive diagnostics can identify problems that could lead to major failures, thus reducing production downtime and maintenance costs. The paper discusses the control and diagnosis of electric motors using prediction techniques. In particular, the use of neural network models and predictive control to improve accuracy and reliability is investigated. The main objective of this research is to develop a neural network controller based on predictive model predictive control (MPC), which will improve the quality of the control and diagnostics system of electric motors, ensuring their stability and preventing possible malfunctions.

Unmanned underwater vehicles (UUV) are widely used tools in ocean development, which can be applied in areas such as marine scientific research, ocean resources exploration, and ocean security. However, as ocean exploration advances, UUVs face increasingly challenging operational environments with weaker communication signals. Consequently, autonomous obstacle avoidance planning for UUVs becomes increasingly important. With the deepening of ocean exploration, the operational environment of UUVs has become increasingly difficult to access, and the communication signals in the environment have become weaker. Therefore, autonomous obstacle avoidance planning of UUVs has become increasingly important. Traditional dynamic programming methods face challenges in terms of accuracy and real-time performance, requiring the design of auxiliary strategies to achieve ideal avoidance and requiring cumbersome perception equipment to support them. Therefore, exploring an efficient and easy-to-implement dynamic programming method has significant theoretical and practical value. In this study, an LSTM-RNN network structure suitable for UUVs was designed to learn the dynamic programming mode of UUVs in an unknown environment. The research was divided into three main aspects: collecting the required sample dataset for training deep networks, designing the LSTM-RNN network structure, and utilizing LSTM-RNN to achieve dynamic programming. Experimental results demonstrated that LSTM-RNN can learn planning patterns in unknown environments without the need for constructing an environment model or complex perception devices, thus providing significant theoretical and practical value. Consequently, this approach offers an effective solution for autonomous obstacle avoidance planning for UUVs.