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Evolutionary Algorithm-Based Complete Coverage Path Planning for Tetriamond Tiling Robots.

Anh Vu Le1,2, Nguyen Huu Khanh Nhan2, Rajesh Elara Mohan1

  • 1ROAR Lab, Engineering Product Development, Singapore University of Technology and Design, Singapore 487372, Singapore.

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Summary
This summary is machine-generated.

This study introduces a novel path planning framework for self-reconfigurable cleaning robots. It optimizes navigation by minimizing shape-shifting and locomotion energy for efficient area coverage.

Keywords:
area coveragecleaning robotenergy awareevolutionary algorithmnavigation planningreconfigurable robot

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computational Geometry

Background:

  • Fixed-morphology robots struggle with area coverage and optimal path planning.
  • Self-reconfigurable robots offer adaptability for narrow spaces and diverse tasks.
  • Navigation energy in reconfigurable robots involves locomotion and shape-shifting.

Purpose of the Study:

  • To develop a framework for optimal navigation path generation in reconfigurable cleaning robots.
  • To minimize shape-shifting requirements for efficient workspace coverage.
  • To integrate locomotion energy costs into the path planning objective function.

Main Methods:

  • Formulating the cleaning environment with tetriamond tiling patterns as waypoints.
  • Defining an energy cost function based on travel distance between waypoints.
  • Optimizing the objective function using evolutionary algorithms (Genetic Algorithm, Ant Colony Optimization) for the Traveling Salesman Problem.

Main Results:

  • A method to generate optimal navigation paths for tetriamond-based reconfigurable robots.
  • Demonstration of minimizing shape-shifting and locomotion energy.
  • The proposed technique is adaptable to other polyamond-based reconfigurable robots.

Conclusions:

  • The developed framework effectively plans optimal navigation paths for reconfigurable cleaning robots.
  • Integrating shape-shifting and locomotion energy leads to more efficient workspace coverage.
  • This approach has potential applications for various polyamond-based robotic systems.