All articles published by MDPI are made immediately available worldwide under an open access license. No special
permission is required to reuse all or part of the article published by MDPI, including figures and tables. For
articles published under an open access Creative Common CC BY license, any part of the article may be reused without
permission provided that the original article is clearly cited. For more information, please refer to
https://www.mdpi.com/openaccess.
Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature
Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for
future research directions and describes possible research applications.
Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive
positive feedback from the reviewers.
Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world.
Editors select a small number of articles recently published in the journal that they believe will be particularly
interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the
most exciting work published in the various research areas of the journal.
Wang, L.; Sun, Z.; Wang, Y.; Wang, J.; Zhao, Z.; Yang, C.; Yan, C.
A Pre-Grasping Motion Planning Method Based on Improved Artificial Potential Field for Continuum Robots. Sensors2023, 23, 9105.
https://doi.org/10.3390/s23229105
AMA Style
Wang L, Sun Z, Wang Y, Wang J, Zhao Z, Yang C, Yan C.
A Pre-Grasping Motion Planning Method Based on Improved Artificial Potential Field for Continuum Robots. Sensors. 2023; 23(22):9105.
https://doi.org/10.3390/s23229105
Chicago/Turabian Style
Wang, Lihua, Zezhou Sun, Yaobing Wang, Jie Wang, Zhijun Zhao, Chengxu Yang, and Chuliang Yan.
2023. "A Pre-Grasping Motion Planning Method Based on Improved Artificial Potential Field for Continuum Robots" Sensors 23, no. 22: 9105.
https://doi.org/10.3390/s23229105
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.