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Robotic Sensing and Stimuli Provision for Guided Plant Growth

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Rapidly Exploring Random Tree Algorithm-Based Path Planning for Worm-Like Robot.

Yifan Wang¹, Prathamesh Pandit¹, Akhil Kandhari¹

¹Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH 44106, USA.

Biomimetics (Basel, Switzerland)

|June 11, 2020

Summary

This summary is machine-generated.

This study introduces an enhanced path planning algorithm for worm-like robots, significantly improving efficiency. The novel approach enables precise navigation and maneuvering, crucial for future robotic applications.

Keywords:

RRT path planning soft robotics worm-like robot

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Area of Science:

Robotics
Biomimetics

Background:

Worm-like robots mimic earthworm locomotion using peristaltic waves.
Existing research focuses on peristalsis wave generation and optimization, neglecting path planning.

Purpose of the Study:

To evaluate rapidly exploring random tree (RRT) algorithms for path planning in worm-like robots.
To develop an efficient path planning strategy for non-holonomic peristaltic locomotion.

Main Methods:

Implemented and evaluated RRT algorithms for worm-like robot path planning.
Integrated an enhanced elliptical path generating algorithm (two-step, forward/backward search).

Main Results:

The enhanced path planner significantly reduced required iterations (by ~2 orders of magnitude).
Enabled calculation of waves for arbitrary position/orientation goals.
Identified configuration space boundaries for forward/backward maneuvering decisions.

Conclusions:

The developed path planner makes planning feasible and efficient for worm-like robots.
Future work should focus on lateral motion strategies and obstacle avoidance for enhanced robot capabilities.