Flexible actuators are popular in the consumer and medical fields because of their flexibility and compliance. However, they are typically difficult to model because of their viscoelasticity and nonlinearity. This letter proposes a method for correcting the deformation of the simulated flexible robots to make it similar to the deformation of real robots using point clouds by deep learning. Long shortterm memory (LSTM) can simulate the next frame of actuator deformation from the previous frames of deformations. In this study, we presented the robots with four different muscle structures. We found that using an encoder-LSTM-decoder network can improve the similarity between the deformation of a learned muscle structure and the real deformation and is also effective in correcting the deformation of the unlearned structures. Our correction method reduced the average Chamfer distance of the simulated point clouds of the basic-type structure actuator from 15.89 to 7.81. This research can provide a new concept for future flexible robot modeling using point clouds.
In recent years, functional fluidic and gas electrohydrodynamic (EHD) pumps have received considerable attention due to their remarkable features, such as simple structure, quiet operation, and energy-efficient utilization. EHD pumps can be applied in various industrial applications, including flow transfer, thermal management, and actuator drive. In this paper, the authors reviewed the literature surrounding functional fluidic and gas EHD pumps regarding the following aspects: the initial observation of the EHD effect, mathematical modeling, and the choice of pump structure, electrode configuration, and working medium. Based on the review, we present a summary of the development and latest research on EHD pumps. This paper provides a critical analysis of the current limitations of EHD pumps and identifies potential areas for future research. Additionally, the potential application of artificial intelligence in the field of EHD pumps is discussed in the context of its cross-disciplinary nature. Many reviews on EHD pumps focus on rigid pumps, and the contribution of this review is to summarize and analyze soft EHD pumps that have received less attention, thus reducing the knowledge gap.
In recent years, deep learning techniques for processing 3D point cloud data have seen significant advancements, given their unique ability to extract relevant features and handle unstructured data. These techniques find wide-ranging applications in fields like robotics, autonomous vehicles, and various other computer-vision applications. This paper reviews the recent literature on key tasks, including 3D object classification, tracking, pose estimation, segmentation, and point cloud completion. The review discusses the historical development of these methods, explores different model architectures, learning algorithms, and training datasets, and provides a comprehensive summary of the state-of-the-art in this domain. The paper presents a critical evaluation of the current limitations and challenges in the field, and identifies potential areas for future research. Furthermore, the emergence of transformative methodologies like PoinTr and SnowflakeNet is examined, highlighting their contributions and potential impact on the field. The potential cross-disciplinary applications of these techniques are also discussed, underscoring the broad scope and impact of these developments. This review fills a knowledge gap by offering a focused and comprehensive synthesis of recent research on deep learning techniques for 3D point cloud data processing, thereby serving as a useful resource for both novice and experienced researchers in the field.
In recent years, functional fluidic electrohydrodynamic (EHD) pumps have attracted considerable attention due to their remarkable features, such as simple structure, quiet operation, and energy-efficient utilization. EHD liquid pumps can be used in various industrial applications, like flow transfer, thermal management, and actuator drive. In this paper, the author reviewed EHD liquid pump research in these specific aspects: the first observation of the EHD effect, its mathematical modeling, and the choice of pump structure, electrode configuration, and working medium. Based on the review, a summary of the development and latest research on EHD pumps can be provided for the readers.
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