Robot system is the key technology for the development of space on-orbit service technology. In view of the relative movement between the manipulator and the target during the capture process, the force sensor is used to obtain the interaction force and torque information between the gripper and the docking ring, and the manipulator opens the zero force control to adjust the pose. At the same time, according to the contact force information between the gripper and the docking ring obtained by the tactile sensor, whether the dual-arm gripper is closed is judged. In this paper, the system modeling and zero force acquisition control of space manipulator are studied under the research background of space failed satellite acquisition mission. Based on the ground microgravity air flotation simulation system, the experimental results show that the space dual-arm zero force capture control system based on force/contact fusion is feasible and it has excellent performance.
Valve, as an important part of pipeline control system, is gradually developing towards the goal of electric and intelligent with the development of technology and the trend of the Internet of Things. In this case, the traditional way of manually supervising the valves and pipes appears to be costly and inefficient and thus is no longer applicable. Therefore, this paper proposes a new way to monitor the status of the valves by developing a remote monitoring and control system for electric valves based on Internet of Things. The system is powered by the wind and solar hybrid electricity generation module and managed by STM32F429. The status data of the valve and related environmental parameters detected by field sensors can be collected by MCU through RS485 and UART and are then encrypted and sent to the remote server through a 4G module. To ensure the security of data transmission, a hybrid encryption algorithm based on ECC and AES is implemented and verified in the embedded system, which uses AES to shorten the encryption time of large amount of plaintext and uses ECC to encrypt the AES key to solve the security shortcomings of AES, thus increasing the security of data transmission.
Part semantic segmentation based on deep learning provides a new insight for accurate vision understanding of noncooperative satellite as well as for further on-orbit servicing tasks like inspection, repair, and close-proximity robotic manipulation. However, carrying out such researches requires a tremendous amount of data, which is extremely hard and expensive in space. Moreover, the manual annotation for fine-grained tasks like segmentation will cost a lot of labor. Thus, in this paper, we present an efficient method of automated synthetic datasets construction for part-level segmentation of non-cooperative satellite, which is capable of generating thousands of multi-source data (RGB image and point cloud) and the corresponding high-quality annotation. Specifically, the Fibonacci lattice is used for multiple viewpoints sampling of the virtual camera to capture RGB-D images. A trick of segmentation of the customized image in HSV color space is applied to get labels automatically. Furthermore, we employ several data augmentation techniques to expand and diversify the datasets, which improves the generalization of the algorithm. Finally, we carry out the case study using the pointnet++ network based on our generated point cloud data, to validate the feasibility and effectiveness of our method.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.