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Reinforcement Learning-Based Complete Area Coverage Path Planning for a Modified hTrihex Robot

One of the essential attributes of a cleaning robot is to achieve complete area coverage. Current commercial indoor cleaning robots have fixed morphology and are restricted to clean only specific areas in a house. The results of maximum area coverage are sub-optimal in this case. Tiling robots are i...

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Autores principales: Apuroop, Koppaka Ganesh Sai, Le, Anh Vu, Elara, Mohan Rajesh, Sheu, Bing J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7913922/
https://www.ncbi.nlm.nih.gov/pubmed/33557225
http://dx.doi.org/10.3390/s21041067
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author Apuroop, Koppaka Ganesh Sai
Le, Anh Vu
Elara, Mohan Rajesh
Sheu, Bing J.
author_facet Apuroop, Koppaka Ganesh Sai
Le, Anh Vu
Elara, Mohan Rajesh
Sheu, Bing J.
author_sort Apuroop, Koppaka Ganesh Sai
collection PubMed
description One of the essential attributes of a cleaning robot is to achieve complete area coverage. Current commercial indoor cleaning robots have fixed morphology and are restricted to clean only specific areas in a house. The results of maximum area coverage are sub-optimal in this case. Tiling robots are innovative solutions for such a coverage problem. These new kinds of robots can be deployed in the cases of cleaning, painting, maintenance, and inspection, which require complete area coverage. Tiling robots’ objective is to cover the entire area by reconfiguring to different shapes as per the area requirements. In this context, it is vital to have a framework that enables the robot to maximize the area coverage while minimizing energy consumption. That means it is necessary for the robot to cover the maximum area with the least number of shape reconfigurations possible. The current paper proposes a complete area coverage planning module for the modified hTrihex, a honeycomb-shaped tiling robot, based on the deep reinforcement learning technique. This framework simultaneously generates the tiling shapes and the trajectory with minimum overall cost. In this regard, a convolutional neural network (CNN) with long short term memory (LSTM) layer was trained using the actor-critic experience replay (ACER) reinforcement learning algorithm. The simulation results obtained from the current implementation were compared against the results that were generated through traditional tiling theory models that included zigzag, spiral, and greedy search schemes. The model presented in the current paper was also compared against other methods where this problem was considered as a traveling salesman problem (TSP) solved through genetic algorithm (GA) and ant colony optimization (ACO) approaches. Our proposed scheme generates a path with a minimized cost at a lesser time.
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spelling pubmed-79139222021-02-28 Reinforcement Learning-Based Complete Area Coverage Path Planning for a Modified hTrihex Robot Apuroop, Koppaka Ganesh Sai Le, Anh Vu Elara, Mohan Rajesh Sheu, Bing J. Sensors (Basel) Article One of the essential attributes of a cleaning robot is to achieve complete area coverage. Current commercial indoor cleaning robots have fixed morphology and are restricted to clean only specific areas in a house. The results of maximum area coverage are sub-optimal in this case. Tiling robots are innovative solutions for such a coverage problem. These new kinds of robots can be deployed in the cases of cleaning, painting, maintenance, and inspection, which require complete area coverage. Tiling robots’ objective is to cover the entire area by reconfiguring to different shapes as per the area requirements. In this context, it is vital to have a framework that enables the robot to maximize the area coverage while minimizing energy consumption. That means it is necessary for the robot to cover the maximum area with the least number of shape reconfigurations possible. The current paper proposes a complete area coverage planning module for the modified hTrihex, a honeycomb-shaped tiling robot, based on the deep reinforcement learning technique. This framework simultaneously generates the tiling shapes and the trajectory with minimum overall cost. In this regard, a convolutional neural network (CNN) with long short term memory (LSTM) layer was trained using the actor-critic experience replay (ACER) reinforcement learning algorithm. The simulation results obtained from the current implementation were compared against the results that were generated through traditional tiling theory models that included zigzag, spiral, and greedy search schemes. The model presented in the current paper was also compared against other methods where this problem was considered as a traveling salesman problem (TSP) solved through genetic algorithm (GA) and ant colony optimization (ACO) approaches. Our proposed scheme generates a path with a minimized cost at a lesser time. MDPI 2021-02-04 /pmc/articles/PMC7913922/ /pubmed/33557225 http://dx.doi.org/10.3390/s21041067 Text en © 2021 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
Apuroop, Koppaka Ganesh Sai
Le, Anh Vu
Elara, Mohan Rajesh
Sheu, Bing J.
Reinforcement Learning-Based Complete Area Coverage Path Planning for a Modified hTrihex Robot
title Reinforcement Learning-Based Complete Area Coverage Path Planning for a Modified hTrihex Robot
title_full Reinforcement Learning-Based Complete Area Coverage Path Planning for a Modified hTrihex Robot
title_fullStr Reinforcement Learning-Based Complete Area Coverage Path Planning for a Modified hTrihex Robot
title_full_unstemmed Reinforcement Learning-Based Complete Area Coverage Path Planning for a Modified hTrihex Robot
title_short Reinforcement Learning-Based Complete Area Coverage Path Planning for a Modified hTrihex Robot
title_sort reinforcement learning-based complete area coverage path planning for a modified htrihex robot
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7913922/
https://www.ncbi.nlm.nih.gov/pubmed/33557225
http://dx.doi.org/10.3390/s21041067
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