An optimized GPU implementation for a path planning algorithm based on parallel pseudo-bacterial potential field
Studies in Computational Intelligence, ISSN: 1860-949X, Vol: 667, Page: 477-492
2017
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Book Chapter Description
This work presents a high-performance implementation of a path planning algorithm based on parallel pseudo-bacterial potential field (parallel-PBPF) on a graphics processing unit (GPU) as an improvement to speed up the path planning computation in mobile robot navigation. Path planning is one of the most computationally intensive tasks in mobile robots and the challenge in dynamically changing environments. We show how data-intensive tasks in mobile robots can be processed efficiently through the use of GPUs. Experiments and simulation results are provided to show the effectiveness of the proposal.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85006086240&origin=inward; http://dx.doi.org/10.1007/978-3-319-47054-2_31; http://link.springer.com/10.1007/978-3-319-47054-2_31; http://link.springer.com/content/pdf/10.1007/978-3-319-47054-2_31; https://dx.doi.org/10.1007/978-3-319-47054-2_31; https://link.springer.com/chapter/10.1007/978-3-319-47054-2_31
Springer Science and Business Media LLC
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