Search

Copyright of Journal of Central China Normal University is the property of Huazhong Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Northeastern University (Natural Science) is the property of Dongbei Daxue Xuebao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the amount of garbage on water surface ever increasing, traditional cleaning methods are no longer able to meet the requirements for safety, efficiency, as well as intelligence in applications. Hence, water surface garbage cleaning robots have emerged and become the focus of development in this field. After more than 20 years of development, it has entered the stage of practical application from research with various structure types designed and the realization of fully autonomous operations from garbage detection to collection. A large amount of literature has been accumulated, but there is still no comprehensive review. Therefore, this paper aims to provide an extensive review of development of water surface garbage cleaning robots in literature. The structures of the water surface garbage cleaning robots are classified with the characteristics of each type summarized. The typical framework of an autonomous water surface garbage cleaning robot is analyzed, and its three key technologies of perception, decision-making, and control are highlighted and discussed. Finally, the paper explores the trends of development for technologies in water surface garbage cleaning robots, which are centered on the challenges presented by current applications, to provide a reference for further research and application of water surface garbage cleaning robots. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the traditional feature extraction method in the unstacking task of industrial box parcels depends on the shape of the box and is difficult to apply to variable specifications and mixed box types, a robot unstacking method based on image instance segmentation is proposed. In order to obtain accurate box picking center, the Mask R-CNN is used to perform instance segmentation to obtain the box mask and classification information, and an STN (spatial transformation network) module is added after feature extraction to optimize the recognition of rotating objects. Then, the minimum circumscribed rectangle of the mask is solved and post-processed to obtain the pixel center and horizontal rotation angle of the box to be picked, and a unstacking strategy is combined to sort first row and then column to determine the picking order. Finally, the calibration method is used to complete the box positioning, and the segmentation and robot unstacking experiments are conducted for variable specifications and mixed box stacking types. The results show that the average pixel distance between the pixel centers of the unboxed body is about 4 pixels, and the average error of spatial location is about 1 cm. The position accuracy meets the actual needs of industrial depalletization. [ABSTRACT FROM AUTHOR]