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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...

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Autores principales: Wang, Yifan, Pandit, Prathamesh, Kandhari, Akhil, Liu, Zehao, Daltorio, Kathryn A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
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.
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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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