This article proposes a ferrofluid soft capsule robot (FSCR) with magnetic actuation for diagnostic and therapeutic medical applications inside the human stomach. A composite shell consisted of ferrofluid, permanent magnet rings, and soft elastomer is developed for the FSCR, which is actuated remotely by an external permanent magnet. The proposed FSCR presents two novel characteristics. First, the composite shell not only has a soft outer surface that reduces damage to contact tissue but also an improved magnetic circuit with a combination of the ferrofluid and permanent magnet rings makes the locomotion of the device more controllable and secure. Second, the FSCR is equipped with a specific oscillation module that enables advanced functions such as drug-releasing. Detailed structure design and fabrication of the FSCR are presented in this study. The surface rolling locomotion of the FSCR is modeled and evaluated by numerical simulations, as well as the drug-releasing function. In order to verify the simulation result, an FSCR prototype is manufactured to perform experimental validation. The analysis result demonstrates the feasibility and efficiency of the proposed FSCR for drug releasing inside a printed human stomach model.

As one of the main pieces of equipment in coal transportation, the belt conveyor with its detection system is an important area of research for the development of intelligent mines. Occurrences of non-coal foreign objects making contact with belts are common in complex production environments and with improper human operation. In order to avoid major safety accidents caused by scratches, deviation, and the breakage of belts, a foreign object detection method is proposed for belt conveyors in this work. Firstly, a foreign object image dataset is collected and established, and an IAT image enhancement module and an attention mechanism for CBAM are introduced to enhance the image data sample. Moreover, to predict the angle information of foreign objects with large aspect ratios, a rotating decoupling head is designed and a MO-YOLOX network structure is constructed. Some experiments are carried out with the belt conveyor in the mine’s intelligent mining equipment laboratory, and different foreign objects are analyzed. The experimental results show that the accuracy, recall, and mAP⁵⁰ of the proposed rotating frame foreign object detection method reach 93.87%, 93.69%, and 93.68%, respectively, and the average inference time for foreign object detection is 25 ms.

Due to the poor lighting conditions and the presence of a large amount of suspended dust in coal mines, obtained video has problems with uneven lighting and low differentiation of facial features. In order to address these problems, an improved image enhancement method is proposed. Firstly, the characteristics of underground coal mine images are analyzed, and median filtering is selected for noise removal. Then, the gamma function and fractional order operator are introduced, and an image enhancement algorithm based on particle swarm optimization is proposed. Finally, several experiments are conducted, and the results show that the proposed improved algorithm outperforms classical image enhancement algorithms, such as MSR, CLAHE and HF. Compared with the original image, the evaluation metrics of the enhanced Yale face images, including average local standard deviation, average gradient, information entropy and contrast, are improved by 113.1%, 63.8%, 22.8% and 24.1%, respectively. Moreover, the proposed algorithm achieves a superior enhancement effect in the simulated coal mine environment.

With the miniaturization of medical equipment and the comfort of diagnosis mode, positioning has become a major challenge in the research of capsule robots. In order to realize the effective positioning of magnetically controlled capsule robots, a positioning method based on double-layer symmetric sensor array is proposed in this work. Firstly, a near-field error correction method based on multi-magnetic dipole model is presented to improve the accuracy of capsule robot magnetic field model. Then, for multi-magnetic driving environment, an arrangement of double-layer symmetric sensor array is designed to reduce the interference of hybrid magnetic fields. The effectiveness of this arrangement is verified by calculating the elimination error of magnetic field in symmetric planes. Driving and positioning device are developed, and a BP neural network model is trained by recording the specific position of the capsule robot and the corresponding magnetic field. The trained BP neural network model can provide a precise initial solution to improve the ability of LM algorithm to solve the position of the capsule robot. Finally, the capsule robot is placed in a porcine stomach in vitro to test the positioning method by gauge points, and the average positioning error is 3.4 mm. The proposed positioning method has high accuracy and meets the needs of clinical inspection of capsule robot.

In order to improve the response characteristics of magnetorheological fluid brake (MRB) and reduce the braking fluctuation rate, an improved grey wolf optimization algorithm was proposed to adjust the parameters of the proportion integration differentiation (PID) controller. Firstly, an MRB system was designed and constructed, and its transfer function was determined. Moreover, by adopting the iterative method of logistic curve, an enhanced grey wolf optimization algorithm (EGWOA) was presented. Using the EGWOA, the parameters of the PID controller were optimized to improve the control performance of the system. Finally, the simulation and experiment were carried out. The results showed that EGWOA has a faster response output and overall better performance without overshoot compared with the conventional PID and grey wolf optimization algorithm (GWOA) PID controller.