Teaching RoboClam to dig: The design, testing, and genetic algorithm optimization of a biomimetic robot
Razor clams (Ensis directus) are one of nature's most adept burrowing organisms, able to dig to 70cm at nearly 1cm/s using only 0.21J/cm. We discovered that Ensis reduces burrowing drag by using motions of its shell to fluidize a thin layer of substrate around its body. We have developed RoboCl...
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers
2013
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| Online Access: | http://hdl.handle.net/1721.1/78659 https://orcid.org/0000-0002-4151-0889 https://orcid.org/0000-0002-5048-4109 https://orcid.org/0000-0001-9755-3856 https://orcid.org/0000-0001-9233-2245 https://orcid.org/0000-0003-4940-7496 |
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| author | Winter, Amos Deits, Robin Lloyd Henderson Dorsch, Daniel S. Hosoi, Anette E. Slocum, Alexander H. |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Winter, Amos Deits, Robin Lloyd Henderson Dorsch, Daniel S. Hosoi, Anette E. Slocum, Alexander H. |
| author_sort | Winter, Amos |
| collection | MIT |
| description | Razor clams (Ensis directus) are one of nature's most adept burrowing organisms, able to dig to 70cm at nearly 1cm/s using only 0.21J/cm. We discovered that Ensis reduces burrowing drag by using motions of its shell to fluidize a thin layer of substrate around its body. We have developed RoboClam, a robot that digs using the same mechanisms as Ensis, to explore how localized fluidization burrowing can be extended to engineering applications. In this work we present burrowing performance results of RoboClam in Ensis' habitat. Using a genetic algorithm to optimize RoboClam's kinematics, the machine was able to burrow at speeds comparable to Ensis, with a power law relationship between digging energy and depth of n = 1.17, close to the n = 1 achieved by the animal. Pushing through static soil has a theoretical energy-depth power law of n = 2, which means that Ensis-inspired digging motions can provide exponential energetic savings over existing burrowing methods. |
| first_indexed | 2024-09-23T12:12:42Z |
| format | Article |
| id | mit-1721.1/78659 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T12:12:42Z |
| publishDate | 2013 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/786592022-09-28T00:44:14Z Teaching RoboClam to dig: The design, testing, and genetic algorithm optimization of a biomimetic robot Winter, Amos Deits, Robin Lloyd Henderson Dorsch, Daniel S. Hosoi, Anette E. Slocum, Alexander H. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Department of Mechanical Engineering Winter, Amos Deits, Robin Lloyd Henderson Dorsch, Daniel S. Hosoi, Anette E. Slocum, Alexander H. Razor clams (Ensis directus) are one of nature's most adept burrowing organisms, able to dig to 70cm at nearly 1cm/s using only 0.21J/cm. We discovered that Ensis reduces burrowing drag by using motions of its shell to fluidize a thin layer of substrate around its body. We have developed RoboClam, a robot that digs using the same mechanisms as Ensis, to explore how localized fluidization burrowing can be extended to engineering applications. In this work we present burrowing performance results of RoboClam in Ensis' habitat. Using a genetic algorithm to optimize RoboClam's kinematics, the machine was able to burrow at speeds comparable to Ensis, with a power law relationship between digging energy and depth of n = 1.17, close to the n = 1 achieved by the animal. Pushing through static soil has a theoretical energy-depth power law of n = 2, which means that Ensis-inspired digging motions can provide exponential energetic savings over existing burrowing methods. Battelle Memorial Institute Chevron Corporation Bluefin Robotics 2013-05-01T18:29:06Z 2013-05-01T18:29:06Z 2010-10 Article http://purl.org/eprint/type/ConferencePaper 978-1-4244-6674-0 2153-0858 INSPEC Accession Number: 11689136 http://hdl.handle.net/1721.1/78659 Winter, A G, R L H Deits, D S Dorsch, A E Hosoi, and A H Slocum. "Teaching RoboClam to Dig: The Design, Testing, and Genetic Algorithm Optimization of a Biomimetic Robot". In 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2010, Pp. 4231–4235. © Copyright 2010 IEEE. https://orcid.org/0000-0002-4151-0889 https://orcid.org/0000-0002-5048-4109 https://orcid.org/0000-0001-9755-3856 https://orcid.org/0000-0001-9233-2245 https://orcid.org/0000-0003-4940-7496 en_US http://dx.doi.org/10.1109/IROS.2010.5654364 Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Institute of Electrical and Electronics Engineers IEEE |
| spellingShingle | Winter, Amos Deits, Robin Lloyd Henderson Dorsch, Daniel S. Hosoi, Anette E. Slocum, Alexander H. Teaching RoboClam to dig: The design, testing, and genetic algorithm optimization of a biomimetic robot |
| title | Teaching RoboClam to dig: The design, testing, and genetic algorithm optimization of a biomimetic robot |
| title_full | Teaching RoboClam to dig: The design, testing, and genetic algorithm optimization of a biomimetic robot |
| title_fullStr | Teaching RoboClam to dig: The design, testing, and genetic algorithm optimization of a biomimetic robot |
| title_full_unstemmed | Teaching RoboClam to dig: The design, testing, and genetic algorithm optimization of a biomimetic robot |
| title_short | Teaching RoboClam to dig: The design, testing, and genetic algorithm optimization of a biomimetic robot |
| title_sort | teaching roboclam to dig the design testing and genetic algorithm optimization of a biomimetic robot |
| url | http://hdl.handle.net/1721.1/78659 https://orcid.org/0000-0002-4151-0889 https://orcid.org/0000-0002-5048-4109 https://orcid.org/0000-0001-9755-3856 https://orcid.org/0000-0001-9233-2245 https://orcid.org/0000-0003-4940-7496 |
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