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- W4226337883 abstract "A* search or other nonuniform-cost searching algorithms are always combined with samplebased planners, such as probabilistic roadmap planner (PRM), to solve 2D navigation problems. However, the combination of uniform-cost searching algorithms and sample-based planners in solving these problems was not fully explored in the past. In this study, programs were implemented to allow the combination of PRM with A* search, breadth-first search, and depth-first search. After the searching algorithms were implemented, experiments were done to test the completeness, optimality, and time of the combinations of the algorithms. In the experiments, the programs were required to find a path for a robot to navigate from the start point to the endpoint in a 2D map with obstacles. This work found that the combination of PRM with A* search was not always complete, but the combinations of PRM with breadth-first search and depth-first search were always complete. After comparing the path planned by three different algorithms, it was determined that the combinations of PRM with breadth-first search and depth-first search were not always optimal, especially when the number of sample points was large. Consequently, A* search is still the most suitable searching algorithm for sample-based planners. This study well explained the shortcomings of combining uniform-cost search algorithms with sample-based planners." @default.
- W4226337883 created "2022-05-05" @default.
- W4226337883 creator A5090571200 @default.
- W4226337883 date "2021-12-01" @default.
- W4226337883 modified "2023-09-27" @default.
- W4226337883 title "The Combination of Searching Algorithms with Sample-Based Planner in Solving 2D Robot Navigation Problems" @default.
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- W4226337883 doi "https://doi.org/10.1109/mlbdbi54094.2021.00102" @default.
- W4226337883 hasPublicationYear "2021" @default.
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