In this thesis we study the problem of pathfinding in static grid-based maps. We apply the approach of abstraction and refinement. We abstract the grid map into a graph representation, and use the classic A* algorithm to search for a path in the abstract space, and then refine it into low-level path.
We started with a 2013 entry program to the Grid-based Path Planning Competition, and implemented several enhancements to experiment with the tradeoff between memory usage and search speed. Our program returns the refined low-level path incrementally, therefore reduces the first-move lag in large maps. We cache the low-level edge paths during runtime to avoid repeatedly refining the same abstract edge. In the precomputation step we calculate the low-level paths for all of the edges in the abstraction and directly access the data during online search. We also applied the weighted A* algorithm for online abstract pathfinding and show that the search speed can be further increased by sacrificing path optimality.
We ran our program with 132 maps and 1,739,340 queries. Results show that caching edge paths increases the search speed by a factor of 4.20 in comparison to returning the path incrementally but without caching. With precomputation, the search speed increases by a factor of 1.00 in comparison to caching edge paths. We show that online pathfinding speed can be increased by using more memory and/or offline storage.
","abstract_html":"<p>In this thesis we study the problem of pathfinding in static grid-based maps. We apply the approach of abstraction and refinement. We abstract the grid map into a graph representation, and use the classic A* algorithm to search for a path in the abstract space, and then refine it into low-level path.</p> <p>We started with a 2013 entry program to the Grid-based Path Planning Competition, and implemented several enhancements to experiment with the tradeoff between memory usage and search speed. Our program returns the refined low-level path incrementally, therefore reduces the first-move lag in large maps. We cache the low-level edge paths during runtime to avoid repeatedly refining the same abstract edge. In the precomputation step we calculate the low-level paths for all of the edges in the abstraction and directly access the data during online search. We also applied the weighted A* algorithm for online abstract pathfinding and show that the search speed can be further increased by sacrificing path optimality.</p> <p>We ran our program with 132 maps and 1,739,340 queries. Results show that caching edge paths increases the search speed by a factor of 4.20 in comparison to returning the path incrementally but without caching. With precomputation, the search speed increases by a factor of 1.00 in comparison to caching edge paths. We show that online pathfinding speed can be increased by using more memory and/or offline storage.</p>","abstract_has_math":false,"creators":["Li, Xin"],"institution":null,"degree_name":"M.S.","degree_level":"Masters Thesis","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Nathan R. Sturtevant, Ph.D.","Scott Leutenegger","Rafael Fajardo"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-01-01T08:00:00Z","date_published":"2016-01-01T08:00:00Z","updated_at":"2026-07-24T02:02:11Z","subjects":["Abstraction and refinement","GPPC","Grid-based path planning competition","Pathfinding","Single agent search","Computer Sciences","Theory and Algorithms"],"languages":["en"],"rights":["Copyright is held by the author. User is responsible for all copyright compliance.
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We started with a 2013 entry program to the Grid-based Path Planning Competition, and implemented several enhancements to experiment with the tradeoff between memory usage and search speed. Our program returns the refined low-level path incrementally, therefore reduces the first-move lag in large maps. We cache the low-level edge paths during runtime to avoid repeatedly refining the same abstract edge. In the precomputation step we calculate the low-level paths for all of the edges in the abstraction and directly access the data during online search. We also applied the weighted A* algorithm for online abstract pathfinding and show that the search speed can be further increased by sacrificing path optimality.
We ran our program with 132 maps and 1,739,340 queries. Results show that caching edge paths increases the search speed by a factor of 4.20 in comparison to returning the path incrementally but without caching. With precomputation, the search speed increases by a factor of 1.00 in comparison to caching edge paths. We show that online pathfinding speed can be increased by using more memory and/or offline storage.
"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Enhancements to Hierarchical Pathfinding Algorithms"]}]}],"canonical_facts":{"dc:contributor":["Nathan R. Sturtevant, Ph.D.","Scott Leutenegger","Rafael Fajardo"],"dc:creator":["Li, Xin"],"dc:date.available":["2017-02-17T08:00:00Z"],"dc:description.abstract":["In this thesis we study the problem of pathfinding in static grid-based maps. We apply the approach of abstraction and refinement. We abstract the grid map into a graph representation, and use the classic A* algorithm to search for a path in the abstract space, and then refine it into low-level path.
We started with a 2013 entry program to the Grid-based Path Planning Competition, and implemented several enhancements to experiment with the tradeoff between memory usage and search speed. Our program returns the refined low-level path incrementally, therefore reduces the first-move lag in large maps. We cache the low-level edge paths during runtime to avoid repeatedly refining the same abstract edge. In the precomputation step we calculate the low-level paths for all of the edges in the abstraction and directly access the data during online search. We also applied the weighted A* algorithm for online abstract pathfinding and show that the search speed can be further increased by sacrificing path optimality.
We ran our program with 132 maps and 1,739,340 queries. Results show that caching edge paths increases the search speed by a factor of 4.20 in comparison to returning the path incrementally but without caching. With precomputation, the search speed increases by a factor of 1.00 in comparison to caching edge paths. We show that online pathfinding speed can be increased by using more memory and/or offline storage.
"],"dc:format":["application/pdf"],"dc:identifier":["https://digitalcommons.du.edu/etd/1209"],"dc:language":["en"],"dc:rights":["Copyright is held by the author. User is responsible for all copyright compliance.
"],"dc:subject":["Abstraction and refinement","GPPC","Grid-based path planning competition","Pathfinding","Single agent search","Computer Sciences","Theory and Algorithms"],"dc:title":["Enhancements to Hierarchical Pathfinding Algorithms"],"thesis:degree_level":["Masters Thesis"],"thesis:degree_name":["M.S."]},"updated_at":"2026-07-24T02:02:11Z"}