We are on the cusp of a new era of connected autonomous vehicles with unprecedented user experiences, tremendously improved road safety and air quality, highly diverse transportation environments and use cases, and a plethora of advanced applications. Realizing this grand vision requires a significantly enhanced vehicle-to-everything (V2X) communication network that should be extremely intelligent and capable of concurrently supporting hyperfast, ultrareliable, and low-latency massive information exchange. It is anticipated that the sixth-generation (6G) communication systems will fulfill these requirements of the next-generation V2X. In this article, we outline a series of key enabling technologies from a range of domains, such as new materials, algorithms, and system architectures. Aiming for truly intelligent transportation systems, we envision that machine learning (ML) will play an instrumental role in advanced vehicular communication and networking. To this end, we provide an overview of the recent advances of ML in 6G vehicular networks. To stimulate future research in this area, we discuss the strength, open challenges, maturity, and enhancing areas of these technologies.

In the automatic welding process on mid/thick plates, the precision of the welding position has an important effect on welding quality, which mainly relies on the identification of the welding seam. However, due to some possible disturbances in complex unstructured welding environments, e.g., strong arc lights, welding splashes, thermal-induced deformations, etc., it is a great challenge to identify the welding seam. In this paper, we propose a robust automatic welding seam identification and tracking method by utilizing structured-light vision. First, after the preprocessing of the welding image, the gray distribution of the laser stripe is tracked and the profile of the welding seam is searched in a small area by using the Kalman filter, with the aim to avoid some disturbances. Second, in order to extract the welding seam profile, a series of centroids obtained by scanning the columns in the rectangular window are fitted using the least-squares method. Third, a character string method is proposed to qualitatively describe the welding seam profile, which might consist of different segment and junction relationship elements. And then, these character strings acquired from the object image are matched with those from the model, so that the position of the welding seam can be determined. Finally, the advantages of the new algorithm are testified and compared through several experiments.

Ultrasonic-based distance measurements using time-of-flight (TOF) is a fundamental technique for different applications across a wide variety of fields. In general, cross correlation between a transmitted and received signal is considered to be the optimal TOF estimation technique, which produces a peak at the time delay between them. Cross correlation provides a superior performance in conjunction with a linear chirp. However, as its accuracy depends on the width of the peak, which is inversely proportional to the signal's bandwidth, it can only be said to be highly accurate if the reflected signal at the receiver is separated in time by more than the width of the correlation peak; otherwise, errors are introduced into the system. To improve its accuracy, the bandwidth of the transmitted signal must be increased, which increases the system cost. In this paper, to solve this problem, a threshold-based phase-correlation technique is proposed, which is able to provide a much narrower peak than cross correlation without increasing the signal's physical bandwidth. To evaluate the proposed method, in a controlled environment, two experiments were performed under low and high multipath conditions. For an operational range of 600 mm (indoor), the root-mean-square errors were [0.10, 0.56] mm and [0.19, 1.19] mm for low and high multipath environments, respectively, which indicate that the proposed technique is precise enough to support high accuracy applications.

This paper aims to resolve the three major issues of fault tolerant control (FTC) for robot manipulators: (i) the faster response, lower tracking errors, lower chattering and higher robustness of the FTC, (ii) the requirement of partial or full knowledge of robot dynamics for the design of model-based FTC, and (iii) the global fixed-time convergence of the system. First, a fixed-time controller based on a backstepping control is designed and its disadvantages are analysed. Then, an adaptive fuzzy backstepping control is developed to enhance the tracking performance of the system. The proposed approach does not require the full prior knowledge of robot dynamic model, thus facilitating implementation of the controller in practical applications. In addition, the tracking errors of the system will be practically convergent within a fixed-time, which provides additional system information in advance. The fixed time convergence of the system is mathematically proved and the performance of the system is demonstrated for FTC of a PUMA560 robot.

Marine debris is a problem both for the health of marine environments and for the human health since tiny pieces of plastic called “microplastics” resulting from the debris decomposition over the time are entering the food chain at any levels. For marine debris detection and removal, autonomous underwater vehicles (AUVs) are a potential solution. In this letter, we focus on the efficiency of AUV vision for real-time marine debris detection. First, we improved the efficiency of a class of state-of-the-art object detectors, namely EfficientDets [1], by 1.5% AP on D0, 2.6% AP on D1, 1.2% AP on D2 and 1.3% AP on D3 without increasing the GPU latency (see Fig. 1 ). Subsequently, we created and made publicly available a dataset for the detection of in-water plastic bags and bottles and trained our improved EfficientDets on this and on two public datasets for marine debris detection. Finally, we began the testing of real-time detection performance on a simulator of marine environments.

In the process of automatic welding based on structured light vision, the precise localization of the welding seam in an image has an important influence on the quality of the welding. However, in practice, there is much interference, such as spatter and arc, which introduces great challenges for accurate welding seam localization. In this paper, we considered welding seam localization problem as visual target tracking and based on that, we proposed a robust welding seam tracking algorithm. Prior to the start of welding, the seam is separated using a cumulative gray frequency, which is utilized to adaptively determine the initial position and size of the search window. During the welding process, large seam motion range may result in only a portion of the welding seam exists in the search window. To prevent that, a tracking-by-detection method is used to calculate the location of the search window. Usually, the intersection of seam and noise, e.g., spatter, has a severe influence on the accuracy of feature points localization. In order to solve this problem, a sequence gravity method (SGM) for extracting a smoother center line of welding seam is proposed, which is able to reduce the impact of interference. The double-threshold recursive least square method is used to fit the curve obtained by SGM with the aim of improving the real-time performance and accuracy of the system. Finally, the superiority of the proposed algorithm is well demonstrated by comparison with other solutions for seam tracking and recognition through extensive experiments.