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Evolutionary Algorithm-Based Complete Coverage Path Planning for Tetriamond Tiling Robots
Tiling robots with fixed morphology face major challenges in terms of covering the cleaning area and generating the optimal trajectory during navigation. Developing a self-reconfigurable autonomous robot is a probable solution to these issues, as it adapts various forms and accesses narrow spaces du...
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/PMC7013451/ https://www.ncbi.nlm.nih.gov/pubmed/31941127 http://dx.doi.org/10.3390/s20020445 |
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author | Le, Anh Vu Nhan, Nguyen Huu Khanh Mohan, Rajesh Elara |
author_facet | Le, Anh Vu Nhan, Nguyen Huu Khanh Mohan, Rajesh Elara |
author_sort | Le, Anh Vu |
collection | PubMed |
description | Tiling robots with fixed morphology face major challenges in terms of covering the cleaning area and generating the optimal trajectory during navigation. Developing a self-reconfigurable autonomous robot is a probable solution to these issues, as it adapts various forms and accesses narrow spaces during navigation. The total navigation energy includes the energy expenditure during locomotion and the shape-shifting of the platform. Thus, during motion planning, the optimal navigation sequence of a self-reconfigurable robot must include the components of the navigation energy and the area coverage. This paper addresses the framework to generate an optimal navigation path for reconfigurable cleaning robots made of tetriamonds. During formulation, the cleaning environment is filled with various tiling patterns of the tetriamond-based robot, and each tiling pattern is addressed by a waypoint. The objective is to minimize the amount of shape-shifting needed to fill the workspace. The energy cost function is formulated based on the travel distance between waypoints, which considers the platform locomotion inside the workspace. The objective function is optimized based on evolutionary algorithms such as the genetic algorithm (GA) and ant colony optimization (ACO) of the traveling salesman problem (TSP) and estimates the shortest path that connects all waypoints. The proposed path planning technique can be extended to other polyamond-based reconfigurable robots. |
format | Online Article Text |
id | pubmed-7013451 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-70134512020-03-09 Evolutionary Algorithm-Based Complete Coverage Path Planning for Tetriamond Tiling Robots Le, Anh Vu Nhan, Nguyen Huu Khanh Mohan, Rajesh Elara Sensors (Basel) Article Tiling robots with fixed morphology face major challenges in terms of covering the cleaning area and generating the optimal trajectory during navigation. Developing a self-reconfigurable autonomous robot is a probable solution to these issues, as it adapts various forms and accesses narrow spaces during navigation. The total navigation energy includes the energy expenditure during locomotion and the shape-shifting of the platform. Thus, during motion planning, the optimal navigation sequence of a self-reconfigurable robot must include the components of the navigation energy and the area coverage. This paper addresses the framework to generate an optimal navigation path for reconfigurable cleaning robots made of tetriamonds. During formulation, the cleaning environment is filled with various tiling patterns of the tetriamond-based robot, and each tiling pattern is addressed by a waypoint. The objective is to minimize the amount of shape-shifting needed to fill the workspace. The energy cost function is formulated based on the travel distance between waypoints, which considers the platform locomotion inside the workspace. The objective function is optimized based on evolutionary algorithms such as the genetic algorithm (GA) and ant colony optimization (ACO) of the traveling salesman problem (TSP) and estimates the shortest path that connects all waypoints. The proposed path planning technique can be extended to other polyamond-based reconfigurable robots. MDPI 2020-01-13 /pmc/articles/PMC7013451/ /pubmed/31941127 http://dx.doi.org/10.3390/s20020445 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 Le, Anh Vu Nhan, Nguyen Huu Khanh Mohan, Rajesh Elara Evolutionary Algorithm-Based Complete Coverage Path Planning for Tetriamond Tiling Robots |
title | Evolutionary Algorithm-Based Complete Coverage Path Planning for Tetriamond Tiling Robots |
title_full | Evolutionary Algorithm-Based Complete Coverage Path Planning for Tetriamond Tiling Robots |
title_fullStr | Evolutionary Algorithm-Based Complete Coverage Path Planning for Tetriamond Tiling Robots |
title_full_unstemmed | Evolutionary Algorithm-Based Complete Coverage Path Planning for Tetriamond Tiling Robots |
title_short | Evolutionary Algorithm-Based Complete Coverage Path Planning for Tetriamond Tiling Robots |
title_sort | evolutionary algorithm-based complete coverage path planning for tetriamond tiling robots |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7013451/ https://www.ncbi.nlm.nih.gov/pubmed/31941127 http://dx.doi.org/10.3390/s20020445 |
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