Hybrid Path Planning Algorithm Based on Membrane Pseudo-Bacterial Potential Field for Autonomous Mobile Robots
A hybrid path planning algorithm based on membrane pseudo-bacterial potential field (MemPBPF) is proposed. Membrane-inspired algorithms can reach an evolutionary behavior based on biochemical processes to find the best parameters for generating a feasible and safe path. The proposed MemPBPF algorith...
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doaj-d82fefe04cf84214a2129db96bc8da092021-03-30T00:20:35ZengIEEEIEEE Access2169-35362019-01-01715678715680310.1109/ACCESS.2019.29498358884165Hybrid Path Planning Algorithm Based on Membrane Pseudo-Bacterial Potential Field for Autonomous Mobile RobotsUlises Orozco-Rosas0https://orcid.org/0000-0002-9627-0093Kenia Picos1https://orcid.org/0000-0001-6203-5389Oscar Montiel2CETYS Universidad, Centro de Innovación y Diseño (CEID), Tijuana, MéxicoCETYS Universidad, Centro de Innovación y Diseño (CEID), Tijuana, MéxicoInstituto Politécnico Nacional, CITEDI-IPN, Tijuana, MéxicoA hybrid path planning algorithm based on membrane pseudo-bacterial potential field (MemPBPF) is proposed. Membrane-inspired algorithms can reach an evolutionary behavior based on biochemical processes to find the best parameters for generating a feasible and safe path. The proposed MemPBPF algorithm uses a combination of the structure and rules of membrane computing. In that sense, the proposed MemPBPF algorithm contains dynamic membranes that include a pseudo-bacterial genetic algorithm for evolving the required parameters in the artificial potential field method. This hybridization between membrane computing, the pseudo-bacterial genetic algorithm, and the artificial potential field method provides an outperforming path planning algorithm for autonomous mobile robots. Computer simulation results demonstrate the effectiveness of the proposed MemPBPF algorithm in terms of path length considering collision avoidance and smoothness. Comparisons with two different versions employing a different number of elementary membranes and with other artificial potential field based algorithms are presented. The proposed MemPBPF algorithm yields improved performance in terms of time execution by using a parallel implementation on a multi-core computer. Therefore, the MemPBPF algorithm achieves high performance yielding competitive results for autonomous mobile robot navigation in complex and real scenarios.https://ieeexplore.ieee.org/document/8884165/Artificial potential fieldautonomous mobile robotsmembrane computingpath planningpseudo-bacterial genetic algorithm |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ulises Orozco-Rosas Kenia Picos Oscar Montiel |
spellingShingle |
Ulises Orozco-Rosas Kenia Picos Oscar Montiel Hybrid Path Planning Algorithm Based on Membrane Pseudo-Bacterial Potential Field for Autonomous Mobile Robots IEEE Access Artificial potential field autonomous mobile robots membrane computing path planning pseudo-bacterial genetic algorithm |
author_facet |
Ulises Orozco-Rosas Kenia Picos Oscar Montiel |
author_sort |
Ulises Orozco-Rosas |
title |
Hybrid Path Planning Algorithm Based on Membrane Pseudo-Bacterial Potential Field for Autonomous Mobile Robots |
title_short |
Hybrid Path Planning Algorithm Based on Membrane Pseudo-Bacterial Potential Field for Autonomous Mobile Robots |
title_full |
Hybrid Path Planning Algorithm Based on Membrane Pseudo-Bacterial Potential Field for Autonomous Mobile Robots |
title_fullStr |
Hybrid Path Planning Algorithm Based on Membrane Pseudo-Bacterial Potential Field for Autonomous Mobile Robots |
title_full_unstemmed |
Hybrid Path Planning Algorithm Based on Membrane Pseudo-Bacterial Potential Field for Autonomous Mobile Robots |
title_sort |
hybrid path planning algorithm based on membrane pseudo-bacterial potential field for autonomous mobile robots |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
A hybrid path planning algorithm based on membrane pseudo-bacterial potential field (MemPBPF) is proposed. Membrane-inspired algorithms can reach an evolutionary behavior based on biochemical processes to find the best parameters for generating a feasible and safe path. The proposed MemPBPF algorithm uses a combination of the structure and rules of membrane computing. In that sense, the proposed MemPBPF algorithm contains dynamic membranes that include a pseudo-bacterial genetic algorithm for evolving the required parameters in the artificial potential field method. This hybridization between membrane computing, the pseudo-bacterial genetic algorithm, and the artificial potential field method provides an outperforming path planning algorithm for autonomous mobile robots. Computer simulation results demonstrate the effectiveness of the proposed MemPBPF algorithm in terms of path length considering collision avoidance and smoothness. Comparisons with two different versions employing a different number of elementary membranes and with other artificial potential field based algorithms are presented. The proposed MemPBPF algorithm yields improved performance in terms of time execution by using a parallel implementation on a multi-core computer. Therefore, the MemPBPF algorithm achieves high performance yielding competitive results for autonomous mobile robot navigation in complex and real scenarios. |
topic |
Artificial potential field autonomous mobile robots membrane computing path planning pseudo-bacterial genetic algorithm |
url |
https://ieeexplore.ieee.org/document/8884165/ |
work_keys_str_mv |
AT ulisesorozcorosas hybridpathplanningalgorithmbasedonmembranepseudobacterialpotentialfieldforautonomousmobilerobots AT keniapicos hybridpathplanningalgorithmbasedonmembranepseudobacterialpotentialfieldforautonomousmobilerobots AT oscarmontiel hybridpathplanningalgorithmbasedonmembranepseudobacterialpotentialfieldforautonomousmobilerobots |
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1724188489918971904 |