Collaborative perception has attracted growing interest from academia and industry due to its potential to enhance perception accuracy, safety, and robustness in autonomous driving through multi-agent information fusion. With the advancement of Vehicle-to-Everything (V2X) communication, numerous collaborative perception datasets have emerged, varying in cooperation paradigms, sensor configurations, data sources, and application scenarios. However, the absence of systematic summarization and comparative analysis hinders effective resource utilization and standardization of model evaluation. As the first comprehensive review focused on collaborative perception datasets, this work reviews and compares existing resources from a multi-dimensional perspective. We categorize datasets based on cooperation paradigms, examine their data sources and scenarios, and analyze sensor modalities and supported tasks. A detailed comparative analysis is conducted across multiple dimensions. We also outline key challenges and future directions, including dataset scalability, diversity, domain adaptation, standardization, privacy, and the integration of large language models. To support ongoing research, we provide a continuously updated online repository of collaborative perception datasets and related literature: https://github.com/frankwnb/Collaborative-Perception-Datasets-for-Autonomous-Driving.

Online sorting robots based on image recognition are key pieces of equipment for the intelligent washing of coal mines. In this paper, a Delta-type, coal gangue sorting, parallel robot is designed to automatically identify and sort scattered coal and gangue on conveyor belts by configuring the image recognition system. Robot calibration technology can reduce the influence of installation error on system accuracy and provides the basis for the robot to accurately track and grab gangue. Due to the fact that the angle deflection error between the conveyor belt coordinate system and the robot coordinate system is not considered in the traditional conveyor belt calibration method, an improved comprehensive calibration method is put forward in this paper. Firstly, the working principle and image recognition and positioning process of the Delta coal gangue sorting robot are introduced. The scale factor parameter Factor_c of the conveyor encoder is adopted to characterize the relationship between the moving distance of the conveyor and the encoder. The conveyor belt calibration experiment is described in detail. The transformation matrix between the camera, the conveyor belt, and the robot are obtained after establishment of the three respective coordinate systems. The experimental results show that the maximum cumulative deviation of traditional calibration method is 13.841 mm and the comprehensive calibration method is 3.839 mm. The main innovation of the comprehensive calibration is such that the accurate position of each coordinate in the robot coordinate system can be determined. This comprehensive calibration method is simple and feasible, and can effectively improve system calibration accuracy and reduce robot installation error on the grasping accuracy. Moreover, a calculation method to eliminate duplicate images is put forward, with the frame rate of the vision system set at seven frames per second to avoid image repetition acquisition and missing images. The experimental results show that this calculation method effectively improves the processing efficiency of the recognition system, thereby meeting the demands of the grab precision of coal gangue separation engineering. The goal revolving around “safety with few people and safety with none” can therefore be achieved in coal gangue sorting using robots.

The Delta robots are widely used in packaging, sorting, precision positioning, and other fields. Motion accuracy is an important indicator for evaluating robot performance. However, due to the existence of mechanism errors, the motion accuracy of the robot will be reduced. Therefore, how to reduce motion errors and improve accuracy are important issues for robots. The purpose of the present study is to analyze the motion error and propose an error compensation scheme to improve the motion accuracy of the robot. Firstly, the kinematic model of the robot is established by the D-H matrix transformation method. An error model considering dimension error, the error of revolute joint clearance, driving error, and the error of spherical joint clearance is established. Additionally, the influence of different errors on the motion accuracy is analyzed. Secondly, an error compensation strategy of controlling the driving angle is proposed. The analysis of error compensation is carried out by a numerical example. Comparing the results before and after compensation, it is known that the robot can move along the desired position, so the notion error of the robot is compensated, which proves that this method is effective for improving the motion accuracy of the robot.

Delta parallel robots are widely used in assembly detection, packaging sorting, precision positioning, and other fields. With the widespread use of robots, people have increasing requirements for motion accuracy and reliability. This paper considers the influence of various mechanism errors on the motion accuracy and analyzes the motion reliability of the mechanism. Firstly, we establish a kinematic model of the robot and obtain the relationship between the position of the end effector and the structural parameters based on the improved D–H transform rule. Secondly, an error model considering the dimension error, the error of revolute joint clearance, driving error, and the error of spherical joint clearance is established. Finally, taking an actual robot as an example, the comprehensive influence of mechanism errors on motion accuracy and reliability in different directions is quantitatively analyzed. It is shown that the driving error is a key factor determining the motion accuracy and reliability. The influence of mechanism errors on motion reliability is different in different directions. The influence of mechanism errors on reliability is small in the vertical direction, while it is great in the horizontal direction. Therefore, we should strictly control the mechanism errors, especially the driving angle, to ensure the motion accuracy and reliability. This research has significance for error compensation, motion reliability analysis, and reliability prediction in robots, and the conclusions can be extended to similar mechanisms.

Aiming at the problems of coal and gangue image recognition in low illumination or high dust concentration environment, an adaptive enhancement method based on improved Retinex algorithm, is proposed: H-GF-MSR algorithm. The algorithm is processed in HSV color space, uses guided filter for multi-scale Retinex algorithm in brightness component, and carries out adaptive saturation stretching in saturation component. Then the images are processed by histogram equalization, and the two algorithms are fused to improve the accuracy. Compared with SSR algorithm, MSRCR algorithm and other classical algorithms, this algorithm is improved in suppressing the generation of halo and image edge enhancement. The experimental results show that in terms of image evaluation indexes such as information entropy, image mean, image average gradient and peak signal-to-noise ratio, compared with SSR algorithm, the algorithm improves by 5.4%, 41.1%, 33.2% and 0.4% respectively; compared with MSRCR algorithm, the average improvement is 2.9%, 32.4%, 31.7% and 0.7% respectively. Through the enhancement experiments of coal gangue image in the illumination range of 10 lux ~ 200 lux, the algorithm has better enhancement effect. It can effectively suppress the bright light and restore the original details of the object.

Springs are critical components in mining vibrating screen elastic supports. However, long-term alternating loads are likely to lead to spring failures, likely resulting in structural damages to the vibrating screen and resulting in a lower separation efficiency. Proper dynamic models provide a basis for spring failure diagnosis. In this paper, a six-degree-of-freedom theoretical rigid body model of a mining vibrating screen is proposed, and a dynamic equation is established in order to explore the dynamic characteristics. Numerical simulations, based on the Newmark-β algorithm, are carried out, and the results indicate that the model proposed is suitable for revealing the dynamic characteristics of the mining vibrating screen. Meanwhile, the mining vibrating screen amplitudes change with the spring failures. Therefore, six types of spring failure are selected for simulations, and the results indicate that the spring failures lead to an amplitude change for the four elastic support points in the x, y, and z directions, where the changes depend on certain spring failures. Hence, the key to spring failure diagnosis lies in obtaining the amplitude change rules, which can reveal particular spring failures. The conclusions provide a theoretical basis for further study and experiments in spring failure diagnosis for a mining vibrating screen.