Special Issue "Space Sampling and Exploration Robotics"
Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 5946
Interests: space exploration; sampling robotics; planetary soil; payload design; intelligent control
Given the technical advantages of unmanned robotics, utilizing intelligent sampling robots to acquire planetary soil samples may be the most reliable and cost-effective solution for future human deep-space exploration. There are several unique challenges in unmanned sampling, such as long-distance time delay, uncertain underground formations, and limited sensor and mass resources; therefore, it is necessary to conduct research to improve the systems’ adaptability to complicated geological formations. Taking the sampling machine's power consumption and the planetary soil’s morphology into account, planetary sampling robots should be entirely closed-loop; they should be able to not only adapt to complicated geological formations, but also detect planetary regolith. From a theoretical viewpoint, space-soil–machine interactions (such as the penetrating, plowing, cutting, drilling, and blasting) involve three-dimensional deformation, unsteady plastic flow, and rates affected by mechanical and environmental coupling, which are challenging problems that must be solved. It should be noted that space soil may contain some unique components such as water, ice, and volatiles. Once the above interaction mechanism is understood, using mechanics models coupled with detecting payloads, uncertain space-soil and rock formations can be explored by unmanned robots, which can collect data on their mechanical, thermal, electrical, and volatile properties. Additionally, by returning these samples to Earth, more accurate results can be acquired. Such research should be applied to the design of space mining machines, to detection of the physical and chemical properties of space soil, and to furthering our understanding of where water comes from and the distribution of water ice in our early solar system.
For this Special Issue, we invite authors to contribute high-quality original research or review papers on planetary regolith and environments, space-soil–machine interaction modeling and validation, sampling robotics and systems, the detection of payloads, in situ resource utilization (ISRU), sensors and actuators in sampling, sampling tool design, in situ intelligent control, and other technologies related to space exploration robotics.
Dr. Junyue Tang
Prof. Dr. Shengyuan Jiang
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Aerospace is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- planetary regolith
- soil–machine interaction
- sampling robotics system
- detecting payloads
- property detection