The increasing use of robot applications in various industries requires close monitoring and management of the data generated by these systems. Therefore, it is essential to implement a monitoring system that identifies and reports abnormal situations in robots. The literature differentiates between rule-based and machine learning methods. Rule-based approaches rely on predefined rules to detect deviations from expected behavior. In contrast, machine learning algorithms acquire the ability to learn patterns on their own. This paper evaluates both approaches to determine whether machine learning algorithms can replace or enhance rule-based methods. The evaluation employs an actual production scenario that deals with three induced anomalies: Additional weight, drop of the manipulated object, and reduced speed.
Material failure is frequently caused by material corrosion. To expedite maintenance by enabling more accurate identification of corroded areas in materials, this study employs a corrosion target detection image dataset, using 12 types of image enhancement and image filtering methods to preprocess the image dataset and using the You Only Look Once (Yolo)v8n model for image detection on each preprocessing dataset. The simulation's findings demonstrate that in contrast to Yolov5n and Yolov6n, Yolov8n can achieve higher Precision, Recall, mAP50, and mAP50-95 values. Simultaneously, maximum Recall and mAP50 values were obtained using color enhancement with a multiplier of 1.5, maximum mAP50-95 value was obtained using median filtering with a convolutional kernel size of 3x3, and maximum Precision value was obtained using minimum filtering with a convolutional kernel size of 5x5. The above results illustrate that selecting appropriate image preprocessing methods can further improve the yolov8 model for identifying corrosion areas in the image. Thus, these findings can provide a certain reference value for the field of corrosion detection.
This paper develops a three-degree-of-freedom rehabilitation robot and establishes its mathematical model along with a Permanent Magnet Synchronous Motor (PMSM) servo control system. Using Simulink software, the current-loop control is implemented to achieve torque control and perform simulation analysis. Additionally, to overcome the effects of gravity, friction, and inertia on the motor during rehabilitation, compliance control is edited via the teach pendant to ensure smooth operation. Motor data, including rotational angle, angular speed, and instantaneous current changes, are captured during the process. Finally, the measured data from the robot's drive system are compared with the Simulink simulation results to verify the effectiveness of the proposed approach.
In order to solve the problem of low accuracy of traditional UAV detection due to RF failure, electromagnetic interference, complex scenes, etc., this paper proposes a UAV detection method based on transfer learning and YOLOv5-Lite model, aiming to carry out efficient and accurate multi-target detection through computer vision technology. In this paper, a diverse dataset containing flying targets such as drones and birds is constructed, and the pre-trained model is optimized and trained on the self-built dataset enriched by data augmentation technology, so that it can cope with UAV detection tasks in various complex natural scenes. Experimental results show that YOLOv5-Lite has excellent performance in complex environments, especially in multi-target and small-target detection tasks, with high accuracy, low false alarm rate and fast inference ability. Compared with similar models, the proposed method has significant advantages in terms of detection accuracy, inference speed and model parameter quantity, and is especially suitable for scenarios with high real-time and resource-constrained applications.
In modern unmanned vehicle technology, outdoor patrolling algorithms play a crucial role in enabling autonomous navigation. To enhance the performance of unmanned vehicles in complex outdoor environments, researchers have been continuously refining and optimizing these algorithms. This paper presents a detailed overview of our approach, including the design of the autonomous navigation camera algorithm, vision processing, and recognition technologies. To address challenges such as limited computational resources, environmental interference in line patrolling, and the prolonged debugging time required for the line patrolling algorithm, we propose innovative solutions. These include multi-threading, segmented adaptive binarization, the edge inheritance method, the least squares approach, and enhancements to the YOLOv5-Lite model. Together, these advancements significantly improve the vehicle's adaptability to diverse lane conditions.
