This paper investigates the autonomous follower robot in the context of human-machine integration. A robust and efficient robot decision module is proposed to recognize, locate, and re-identify the target while handling situations of lost targets. First, the DBSCAN clustering algorithm is employed to recognize and locate the target based on point cloud data acquired by the single-line lidar. To identify the target pedestrian, the location information from Ultra-Wide Band (UWB) is utilized. Sensor data fusion with nonlinear least squares optimization enables the extraction of coordinate information in the lidar coordinate system. Subsequently, coordinate transformation is applied to obtain the position information in the robot's coordinate system. To enhance the robustness of the mobile robot's following process, a multimodal behavior controller is proposed based on the data from two sensors. This controller allows the robot to switch between different modes, facilitating the retrieval of the target even under external disturbances. Experimental validation demonstrates the effectiveness of the proposed multi-sensor fusion method in reducing the tracking error of the mobile robot by 55%.
Neural Radiation Field (NeRF) is driving the development of 3D reconstruction technology. Several NeRF variants have been proposed to improve rendering accuracy and reconstruction speed. One of the most significant variants, TensoRF, uses a 4D tensor to model the radiation field, resulting in improved accuracy and speed. However, reconstruction quality remains limited. This study presents an improved TensoRF that addresses the aforementioned issues by reconstructing its multilayer perceptron network. Increasing the number of neurons in the input and network layers improves the render accuracy. To accelerate the reconstruction speed, we utilized the Nadam optimization algorithm and the RELU6 activation function. Our experiments on various classical datasets demonstrate that the PSNR value of the improved TensoRF is higher than that of the original TensoRF. Additionally, the improved TensoRF has a faster reconstruction speed (≤30min). Finally, we applied the improved TensoRF to a self-made industrial dataset. The results showed better global accuracy and local texture in the reconstructed image.
Traditional steel pipe counting can only be performed manually, which has the problems of large workload, error prone and low efficiency. This paper proposes a steel pipe counting system based on image recognition. First of all, the steel pipe image is collected from the camera, and then the digital image processing technology is used to perform image enhancement, edge detection and morphological operation and other preprocessing, then the steel pipe is identified and counted by the Hough circle transformation, and finally the steel pipe automatic is developed using VC++ software Technical software. Experiments show that the automatic counting accuracy and efficiency of steel pipes in this system are high.
KEYWORDS: Operating systems, Laser processing, Data conversion, Mobile robots, Particles, Transform theory, Laser range finders, Data processing, Distance measurement, Data acquisition
Laser SLAM can be implemented using ROS and Ubuntu system. However, it cannot be run in Windows operating system which is more stable than Ubuntu. To implement the laser SLAM in Windows system, the main program of laser SLAM in ROS is carefully analyzed and modified to make it adapt to Windows system. The main programs of laser processing, coordinate transformation and map construction are rewritten and reorganized. To verify the effectiveness of our work, experiments were conducted in real-world environments. The results of experiments validated that laser SLAM can be implemented in Windows system by rewriting and reorganizing these main programs.
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