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Title: 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 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.  more » « less
Award ID(s):
1850168
PAR ID:
10252874
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Biomimetics
Volume:
5
Issue:
2
ISSN:
2313-7673
Page Range / eLocation ID:
26
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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