دورية أكاديمية
Branching Vine Robots for Unmapped Environments.
| العنوان: | Branching Vine Robots for Unmapped Environments. |
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| المؤلفون: | Glick PE; Department of Mechanical and Aerospace Engineering, University of California, San Diego, San Diego, CA, United States.; NASA Jet Propulsion Laboratory (JPL), La Cañada Flintridge, CA, United States., Adibnazari I; Department of Mechanical and Aerospace Engineering, University of California, San Diego, San Diego, CA, United States., Drotman D; Department of Mechanical and Aerospace Engineering, University of California, San Diego, San Diego, CA, United States., Ruffatto Iii D; NASA Jet Propulsion Laboratory (JPL), La Cañada Flintridge, CA, United States., Tolley MT; Department of Mechanical and Aerospace Engineering, University of California, San Diego, San Diego, CA, United States. |
| المصدر: | Frontiers in robotics and AI [Front Robot AI] 2022 Mar 24; Vol. 9, pp. 838913. Date of Electronic Publication: 2022 Mar 24 (Print Publication: 2022). |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Frontiers Media SA Country of Publication: Switzerland NLM ID: 101749350 Publication Model: eCollection Cited Medium: Internet ISSN: 2296-9144 (Electronic) Linking ISSN: 22969144 NLM ISO Abbreviation: Front Robot AI Subsets: PubMed not MEDLINE |
| أسماء مطبوعة: | Original Publication: Lausanne, Switzerland : Frontiers Media SA, [2014]- |
| مستخلص: | While exploring complex unmapped spaces is a persistent challenge for robots, plants are able to reliably accomplish this task. In this work we develop branching robots that deploy through an eversion process that mimics key features of plant growth (i.e., apical extension, branching). We show that by optimizing the design of these robots, we can successfully traverse complex terrain even in unseen instances of an environment. By simulating robot growth through a set of known training maps and evaluating performance with a reward heuristic specific to the intended application (i.e., exploration, anchoring), we optimized robot designs with a particle swarm algorithm. We show these optimization efforts transfer from training on known maps to performance on unseen maps in the same type of environment, and that the resulting designs are specialized to the environment used in training. Furthermore, we fabricated several optimized branching everting robot designs and demonstrated key aspects of their performance in hardware. Our branching designs replicated three properties found in nature: anchoring, coverage, and reachability. The branching designs were able to reach 25% more of a given space than non-branching robots, improved anchoring forces by 12.55×, and were able to hold greater than 100× their own mass (i.e., a device weighing 5 g held 575 g). We also demonstrated anchoring with a robot that held a load of over 66.7 N at an internal pressure of 50 kPa. These results show the promise of using branching vine robots for traversing complex and unmapped terrain. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2022 Glick, Adibnazari, Drotman, Ruffatto III and Tolley.) |
| References: | Soft Robot. 2019 Feb;6(1):95-108. (PMID: 30339050) Soft Robot. 2018 Aug;5(4):443-451. (PMID: 29924697) Proc Natl Acad Sci U S A. 2021 Feb 23;118(8):. (PMID: 33608460) Soft Robot. 2017 Sep 1;4(3):211-223. (PMID: 29062628) IEEE Trans Robot. 2016 Dec;32(6):1419-1430. (PMID: 28713227) Sci Robot. 2017 Jul 19;2(8):. (PMID: 33157883) |
| فهرسة مساهمة: | Keywords: design; eversion; optimization; soft robotics; vine robots |
| تواريخ الأحداث: | Date Created: 20220411 Latest Revision: 20220413 |
| رمز التحديث: | 20221213 |
| مُعرف محوري في PubMed: | PMC8987124 |
| DOI: | 10.3389/frobt.2022.838913 |
| PMID: | 35402519 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 2296-9144 |
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| DOI: | 10.3389/frobt.2022.838913 |