Ever since the space tether was first proposed by Tsiolkovsky, it has been extensively utilized in space missions, for attitude stabilization, momentum exchange, and space elevators. Developments in engineering technology and changes in the space environment have diversified the current applications for the space tether. New applications for the space tether include the Tethered Space Robot, Tethered Space Net, and Tethered Spacecraft Formation. These are quickly being adapted for in-orbit maintenance such as fueling service, orbit maneuvering, and active space debris capture/removal. The flexibility and elasticity of the space tether lead to complex issues with tethered space systems, including the mechanics design, dynamics modeling and analysis, and control scheme design. In this paper, we review several new applications for the space tether during service in orbit, and research the on structure, dynamics, and control of each application. This review is conducted to provide an overall summary of the space tether for On-Orbit Servicing, and further the conversation regarding possible research interests in the future.

A review of space tether in new applications

With the rapid development of machine learning, its powerful function in the machine vision field is increasingly reflected. The combination of machine vision and robotics to achieve the same precise and fast grasping as that of humans requires high-precision target detection and recognition, location and reasonable grasp strategy generation, which is the ultimate goal of global researchers and one of the prerequisites for the large-scale application of robots. Traditional machine learning has a long history and good achievements in the field of image processing and robot control. The CNN (convolutional neural network) algorithm realizes training of large-scale image datasets, solves the disadvantages of traditional machine learning in large datasets, and greatly improves accuracy, thereby positioning CNNs as a global research hotspot. However, the increasing difficulty of labeled data acquisition limits their development. Therefore, unsupervised learning, self-supervised learning and reinforcement learning, which are less dependent on labeled data, have also undergone rapid development and achieved good performance in the fields of image processing and robot capture. According to the inherent defects of vision, this paper summarizes the research achievements of tactile feedback in the fields of target recognition and robot grasping and finds that the combination of vision and tactile feedback can improve the success rate and robustness of robot grasping. This paper provides a systematic summary and analysis of the research status of machine vision and tactile feedback in the field of robot grasping and establishes a reasonable reference for future research.

/pdf/object-detection-recognition-and-robot-grasping-based-on-3omlo05jkl.pdf

Object Detection Recognition and Robot Grasping Based on Machine Learning: A Survey

This paper proposes a new one-dimensional chaotic map, called chaotic coupled Sine map (CCSM), for the scrambling and encryption processes. The purpose of the CCSM design is to increase the security of the key space relative to the basic maps, such as Logistic map and Sine map. Bifurcation diagram and Lyapunov exponent analysis showed that the proposed map is fully chaotic and can be used as a secure pseudo-random number generator in encryption systems. Furthermore, to increase the flexibility of the proposed method, a dynamic algorithm based on the size of blocks is used in the scrambling process. To compare the proposed method and some similar schemes, a simple encryption process is used. The simulation results of this algorithm are evaluated based on security and performance analysis criteria. The comparison results show that the suggested technique has better performance in terms of security and visual quality measure.

Digital image scrambling based on a new one-dimensional coupled Sine map

The development of space robots is vital to broadening human cognitive boundaries. Space robots have been deployed in space science experiments, extravehicular operations, and deep space exploration. The application of space robots undoubtedly reduces the risk and cost of space activities. Traditional space robots primarily utilize rigid structures, resulting in limited degrees of freedom, which restricts their operational capabilities. In contrast, soft robots with greater flexibility and robustness may be used for future space exploration. Soft robots applied in space environments must overcome significant challenges associated with ultrahigh vacuum, microgravity, extreme temperatures, and high‐energy radiation. Herein, a comprehensive analysis of the key advantages of soft robots is presented based on the special requirements of the space environments for soft robots. Furthermore, brief insights into how soft robots must be changed in terms of their design, modeling, fabrication, sensing, and control to adapt to space environments are discussed. Specifically, soft robot scenarios with potential space application value are introduced. Finally, opinions regarding the potential directions of soft space robots are provided.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/aisy.202200071

Progress, Challenges, and Prospects of Soft Robotics for Space Applications

In this paper, we analyze the behaviours of two different fractional derivative operators defined in the last decade. One of them is defined with the normalized sinc function (NSF) and the other one is defined with the Mittag-Leffler function (MLF). Both of them have a non-singular kernel. The fractional derivative operator defined with the MLF is developed by Atangana and Baleanu (ABO) in 2016 and the other operator defined with the normalized sinc function (NSFDO) is created by Yang et al. in 2017. These mentioned operators have some advantages to model the real life problems and to solve them. On the other hand, since the Laplace transform (LT) of the ABO can be calculated more easily, it can be preferred to solve linear/nonlinear problems. In this study, we use the perturbation method with coupled the LTs of these operators to analyze their performance in solving some fractional differential equations. Furthermore, by constructing the error analysis, we test the practicability and usefulness of the method.

Characterizations of two different fractional operators without singular kernel

A non-cooperative target grasping position prediction model for tethered space robot

Convolutional multi-grasp detection using grasp path for RGBD images

An efficient circle detector not relying on edge detection

In the ultra-close approaching phase of tethered space robot, a highly stable self-attitude control is essential. However, due to the field of view limitation of cameras, typical point features are difficult to extract, where commonly adopted position-based visual servoing cannot be valid anymore. To provide robot’s relative position and attitude with the target, we propose a monocular visual servoing control method using only the edge lines of satellite brackets. Firstly, real time detection of edge lines is achieved based on image gradient and region growing. Then, we build an edge line based model to estimate the relative position and attitude between the robot and the target. Finally, we design a visual servoing controller combined with PD controller. Experimental results demonstrate that our algorithm can extract edge lines stably and adjust the robot’s attitude to satisfy the grasping requirements.

/pdf/autonomous-rendezvous-and-docking-with-nonfull-field-of-view-5cqogzr51x.pdf

Autonomous Rendezvous and Docking with Nonfull Field of View for Tethered Space Robot

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Chen Lu

Author Tools

Chat about Author