@inbook{ca4baf6916454271b3fb08d2bed4197f,
title = "GPU Accelerated Membrane Evolutionary Artificial Potential Field for Mobile Robot Path Planning",
abstract = "This work presents a graphics processing unit (GPU) accelerated membrane evolutionary artificial potential field (MemEAPF) algorithm implementation for mobile robot path planning. Three different implementations are compared to show the performance, effectiveness, and efficiency of the MemEAPF algorithm. Simulation results for the three different implementations of the MemEAPF algorithm, a sequential implementation on CPU, a parallel implementation on CPU using the open multi-processing (OpenMP) application programming interface, and the parallel implementation on GPU using the compute unified device architecture (CUDA) are provided to validate the comparative and analysis. Based on the obtained results, we can conclude that the GPU implementation is a powerful way to accelerate the MemEAPF algorithm because the path planning problem in this work has been stated as a data-parallel problem.",
keywords = "Artificial potential field, Genetic algorithms, Graphics processing unit, Membrane computing, Mobile robots, Path planning",
author = "Ulises Orozco-Rosas and Kenia Picos and Oscar Montiel and Oscar Castillo",
note = "Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.",
year = "2021",
doi = "10.1007/978-3-030-68776-2_13",
language = "Ingl{\'e}s",
series = "Studies in Computational Intelligence",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "233--247",
booktitle = "Studies in Computational Intelligence",
address = "Alemania",
}