Dynamic Adjustment Strategy of Rail Guide Vehicle

For the dynamic adjustment strategy of intelligent rail guide vehicle, the dynamic planning model has been built with the longest working time in the paper. In solving this problem, the longest target of the rail guide vehicles’ running time is converted to find the optimal moving path of the rail guide vehicles. Drawing on the shortest path idea, the model uses the Floyd algorithm and the simulated annealing algorithm. The longest running time of the rail guide vehicles in the model is the innovation point in the paper. When the rail guide vehicles path is optimal, when the rail guide vehicles is the busiest, the system is the most efficient.

Security-Oriented Indoor Robots Tracking: An Object Recognition Viewpoint

Indoor robots, in particular AI-enhanced robots, are enabling a wide range of beneficial applications. However, great cyber or physical damages could be resulted if the robots’ vulnerabilities are exploited for malicious purposes. Therefore, a continuous active tracking of multiple robots’ positions is necessary. From the perspective of wireless communication, indoor robots are treated as radio sources. Existing radio tracking methods are sensitive to indoor multipath effects and error-prone with great cost. In this backdrop, this paper presents an indoor radio sources tracking algorithm. Firstly, an RSSI (received signal strength indicator) map is constructed based on the interpolation theory. Secondly, a YOLO v3 (You Only Look Once Version 3) detector is applied on the map to identify and locate multiple radio sources. Combining a source’s locations at different times, we can reconstruct its moving path and track its movement. Experimental results have shown that in the typical parameter settings, our algorithm’s average positioning error is lower than 0.39 m, and the average identification precision is larger than 93.18% in case of 6 radio sources.

High-Precision 3D Reconstruction of Cooperative Markers under Motion Blur

The application of artificial intelligence and deep learning in the fields of wireless communication, image and speech recognition, and 3D reconstruction has successfully solved some difficult modeling problems. This paper focuses on the high-precision 3D reconstruction of the motion-blurred cooperative markers, including the Chinese character coded targets (CCTs) and the noncoded circular markers. A simulation-based motion-blurred image generation model is constructed to provide sufficient samples for training the convolutional neural network to identify and match the motion-blurred CCTs on the moving object. The blurred noncoded marker matching is performed through homography. The 3D reconstruction of the markers is realized via the optimization of the spatial moving path within the exposure period. The midpoint of the moving path of the markers is taken as the final reconstruction result. The experimental results show that the 3D reconstruction accuracy of the markers with a certain motion blur effect is about 0.08 mm.

A Case Study of Whistle Detection and Localization for Humpback Dolphins in Taiwan

In recent years, Taiwan’s government has focused on policies regarding offshore wind farming near the Indo-Pacific humpback dolphin habitat, where marine mammal observation is a critical consideration. The present research developed an algorithm called National Taiwan University Passive Acoustic Monitoring (NTU_PAM) to assist marine mammal observers (MMOs). The algorithm performs whistle detection processing and whistle localization. Whistle detection processing is based on image processing and whistle feature extraction; whistle localization is based on the time difference of arrival (TDOA) method. To test the whistle detection performance, we used the same data to compare NTU_PAM and the widely used software PAMGuard. To test whistle localization, we designed a real field experiment where a sound source projected simulated whistles, which were then recorded by several hydrophone stations. The data were analyzed to locate the moving path of the source. The results show that localization accuracy was higher when the sound source position was in the detection region composed of hydrophone stations. This paper provides a method for MMOs to conveniently observe the migration path and population dynamics of cetaceans without ecological disturbance.

Automatic Creation of Heuristic-Based Truck Movement Paths for Construction Equipment Control

The demand for technologies related to smart construction is rising as the need for increased productivity in the field of construction becomes ever more important. The fourth industrial revolution has accelerated the growth of advanced technologies, including artificial intelligence, 5G, the internet of things, and more, and these technologies are key in relation to smart construction. During the construction phase of our study, a truck travelled along a temporary road within a construction site. Thus, optimizing the location of the temporary road enhances the truck’s moving path, resulting in increased productivity in the road construction process. Thus, in this work, the concept of automating a path for construction equipment (a truck) is proposed. The construction site was divided into cells, where five factors were suggested to create a cost model that could automatically be used to create one of the most efficient paths for construction equipment. With the proposed concept, one can automatically create one of the most efficient paths when deciding the location for a temporary road during construction.

A Moving Path Tracking Method of the Thunderstorm Cloud Based on the Three-Dimensional Atmospheric Electric Field Apparatus

In order to obtain the position of thunderstorm cloud in real time and make it possible to track the thunderstorm cloud motion, a method is proposed for tracking the moving path of thunderstorm cloud, with the aid of the three-dimensional atmospheric electric field apparatus (AEFA). According to the method of images, we establish a spatial model for tracking the moving path. Based on the model, we define the dynamic parameters of thunderstorm cloud position. Subsequently, to realize the moving path tracking of thunderstorm cloud, its coordinates are associated with the time points. Besides, we use the relationship between electric field component measurement error, horizontal angle, elevation angle, and the tracking accuracy to analyze the tracking performance. Finally, a fusion system combining an electric field measurement unit, electric field calibration unit, and permittivity measurement unit is designed to meet the actual needs. The results show that the method can accurately track the thunderstorm cloud moving path and has a better effect. In addition, the method can also be combined with a radar map, thus better predicting the development of the thunderstorm cloud.