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Rapidly Exploring Random Tree Algorithm-Based Path Planning for Worm-Like Robot
Inspired by earthworms, worm-like robots use peristaltic waves to locomote. While there has been research on generating and optimizing the peristalsis wave, path planning for such worm-like robots has not been well explored. In this paper, we evaluate rapidly exploring random tree (RRT) algorithms f...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7345883/ https://www.ncbi.nlm.nih.gov/pubmed/32517012 http://dx.doi.org/10.3390/biomimetics5020026 |
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author | Wang, Yifan Pandit, Prathamesh Kandhari, Akhil Liu, Zehao Daltorio, Kathryn A. |
author_facet | Wang, Yifan Pandit, Prathamesh Kandhari, Akhil Liu, Zehao Daltorio, Kathryn A. |
author_sort | Wang, Yifan |
collection | PubMed |
description | Inspired by earthworms, worm-like robots use peristaltic waves to locomote. While there has been research on generating and optimizing the peristalsis wave, path planning for such worm-like robots has not been well explored. In this paper, we evaluate rapidly exploring random tree (RRT) algorithms for path planning in worm-like robots. The kinematics of peristaltic locomotion constrain the potential for turning in a non-holonomic way if slip is avoided. Here we show that adding an elliptical path generating algorithm, especially a two-step enhanced algorithm that searches path both forward and backward simultaneously, can make planning such waves feasible and efficient by reducing required iterations by up around 2 orders of magnitude. With this path planner, it is possible to calculate the number of waves to get to arbitrary combinations of position and orientation in a space. This reveals boundaries in configuration space that can be used to determine whether to continue forward or back-up before maneuvering, as in the worm-like equivalent of parallel parking. The high number of waves required to shift the body laterally by even a single body width suggests that strategies for lateral motion, planning around obstacles and responsive behaviors will be important for future worm-like robots. |
format | Online Article Text |
id | pubmed-7345883 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-73458832020-07-09 Rapidly Exploring Random Tree Algorithm-Based Path Planning for Worm-Like Robot Wang, Yifan Pandit, Prathamesh Kandhari, Akhil Liu, Zehao Daltorio, Kathryn A. Biomimetics (Basel) Article Inspired by earthworms, worm-like robots use peristaltic waves to locomote. While there has been research on generating and optimizing the peristalsis wave, path planning for such worm-like robots has not been well explored. In this paper, we evaluate rapidly exploring random tree (RRT) algorithms for path planning in worm-like robots. The kinematics of peristaltic locomotion constrain the potential for turning in a non-holonomic way if slip is avoided. Here we show that adding an elliptical path generating algorithm, especially a two-step enhanced algorithm that searches path both forward and backward simultaneously, can make planning such waves feasible and efficient by reducing required iterations by up around 2 orders of magnitude. With this path planner, it is possible to calculate the number of waves to get to arbitrary combinations of position and orientation in a space. This reveals boundaries in configuration space that can be used to determine whether to continue forward or back-up before maneuvering, as in the worm-like equivalent of parallel parking. The high number of waves required to shift the body laterally by even a single body width suggests that strategies for lateral motion, planning around obstacles and responsive behaviors will be important for future worm-like robots. MDPI 2020-06-05 /pmc/articles/PMC7345883/ /pubmed/32517012 http://dx.doi.org/10.3390/biomimetics5020026 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wang, Yifan Pandit, Prathamesh Kandhari, Akhil Liu, Zehao Daltorio, Kathryn A. Rapidly Exploring Random Tree Algorithm-Based Path Planning for Worm-Like Robot |
title | Rapidly Exploring Random Tree Algorithm-Based Path Planning for Worm-Like Robot |
title_full | Rapidly Exploring Random Tree Algorithm-Based Path Planning for Worm-Like Robot |
title_fullStr | Rapidly Exploring Random Tree Algorithm-Based Path Planning for Worm-Like Robot |
title_full_unstemmed | Rapidly Exploring Random Tree Algorithm-Based Path Planning for Worm-Like Robot |
title_short | Rapidly Exploring Random Tree Algorithm-Based Path Planning for Worm-Like Robot |
title_sort | rapidly exploring random tree algorithm-based path planning for worm-like robot |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7345883/ https://www.ncbi.nlm.nih.gov/pubmed/32517012 http://dx.doi.org/10.3390/biomimetics5020026 |
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