Path Counting for Grid-Based Navigation
Authors: Rhys Goldstein, Kean Walmsley, Jacobo Bibliowicz, Alexander Tessier, Simon Breslav, Azam Khan
JAIR 2022 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then collected statistics for the four basic methods below: Regular A*: The existing grid-based A* implementation in which a shortest grid path is arbitrarily selected by way of various tie-breaking conventions. ... The tests were conducted using benchmark sets from the repository of Sturtevant (2012). |
| Researcher Affiliation | Industry | Rhys Goldstein EMAIL Kean Walmsley EMAIL Jacobo Bibliowicz EMAIL Alexander Tessier EMAIL Autodesk Research, Toronto, ON, Canada Simon Breslav EMAIL Azam Khan EMAIL Trax.Co, Toronto, ON, Canada |
| Pseudocode | Yes | Algorithm 1 formalizes the above strategy for generating a central grid path using data from an all-paths Dijkstra or A* search. ... Algorithm 2 defines the function that starts at a given source vertex and counts shortest grid paths represented by the supplied successors. |
| Open Source Code | No | The paper states: 'We retrieved a copy of the open source any-angle path planning C++ library by Uras and Koenig (2015), and extended it with a Central A* implementation.' While it cites the Uras and Koenig library as open source, it does not explicitly state that the authors' 'extended' Central A* implementation is also released as open source, nor does it provide a direct link to their own code. |
| Open Datasets | Yes | The tests were conducted using benchmark sets from the repository of Sturtevant (2012). Each set is a collection of maps containing 2D grid-based obstacle geometry, and each map has an associated list of path planning problems specifying start and end vertices. |
| Dataset Splits | No | The paper mentions 'benchmark sets' and 'problem sets' that were 'updated in 2018', with 'each map has an associated list of path planning problems specifying start and end vertices.' However, it does not provide specific details on how these problems or maps are split into training, validation, or test sets for their experiments. |
| Hardware Specification | Yes | Tests were run on a 2.7GHz Intel Core i7 laptop with 16GB of RAM. |
| Software Dependencies | No | The paper mentions using 'the open source any-angle path planning C++ library by Uras and Koenig (2015)' but does not provide specific version numbers for this library or any other software dependencies. |
| Experiment Setup | No | The paper describes the algorithms and their theoretical properties, as well as general aspects like using the 'standard grid distance heuristic' or 'octile distance' and how the 'C++ library processes the maps by placing vertices on the corners of cells and permitting passage through single-point gaps.' However, it does not provide specific experimental setup details such as hyperparameters (e.g., learning rates, batch sizes, number of epochs) or other system-level training settings. |