Mobile robot path planning using membrane evolutionary artificial potential field

Ulises Orozco-Rosas, Oscar Montiel, Roberto Sepúlveda

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

© 2019 In this paper, a membrane evolutionary artificial potential field (memEAPF) approach for solving the mobile robot path planning problem is proposed, which combines membrane computing with a genetic algorithm (membrane-inspired evolutionary algorithm with one-level membrane structure) and the artificial potential field method to find the parameters to generate a feasible and safe path. The memEAPF proposal consists of delimited compartments where multisets of parameters evolve according to rules of biochemical inspiration to minimize the path length. The proposed approach is compared with artificial potential field based path planning methods concerning to their planning performance on a set of twelve benchmark test environments, and it exhibits a better performance regarding path length. Experiments to demonstrate the statistical significance of the improvements achieved by the proposed approach in static and dynamic environments are shown. Moreover, the implementation results using parallel architectures proved the effectiveness and practicality of the proposal to obtain solutions in considerably less time.
Original languageAmerican English
Pages (from-to)236-251
Number of pages210
JournalApplied Soft Computing Journal
DOIs
StatePublished - 1 Apr 2019

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trajectory planning
potential fields
Motion planning
robots
Mobile robots
membranes
Membranes
proposals
inspiration
membrane structures
Membrane structures
Parallel architectures
compartments
Evolutionary algorithms
genetic algorithms
planning
Genetic algorithms
Planning
Experiments

Cite this

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Mobile robot path planning using membrane evolutionary artificial potential field. / Orozco-Rosas, Ulises; Montiel, Oscar; Sepúlveda, Roberto.

In: Applied Soft Computing Journal, 01.04.2019, p. 236-251.

Research output: Contribution to journalArticle

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