H-TRIP: Exact Many-to-Many Distance Index for Multi-Robot Exploration
H-TRIP: Exact Many-to-Many Distance Index for Multi-Robot Exploration
This paper introduces H-TRIP (Hierarchical Tropical Rank-one Incremental Propagation), a method for efficiently computing exact shortest-path distances in multi-robot exploration scenarios. The approach uses a spatial hierarchy and rank-one updates to handle growing occupancy grids, achieving significant speedups over traditional graph search methods while maintaining accuracy.
The authors present H-TRIP, an exact distance index for multi-robot exploration that achieves up to two orders of magnitude speedup over sequential BFS on dense many-to-many queries while maintaining exact shortest-path distances on growing occupancy grids.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
Robot count
16 robots
View original evidence
experiments confirm exact agreement with reference searches on fully built maps and show that dense many-to-many queries at a few dozen viewpoints are up to two orders of magnitude faster than sequential k x BFS. On exploration runs with sixteen robots, in-loop queries on the growing occupancy graph are 2.21x-8.59x faster than co-scheduled k x BFS on maps with side 256, 512, and 1024 cells.
Open source S5speedup factor
2.21
View original evidence
experiments confirm exact agreement with reference searches on fully built maps and show that dense many-to-many queries at a few dozen viewpoints are up to two orders of magnitude faster than sequential k x BFS. On exploration runs with sixteen robots, in-loop queries on the growing occupancy graph are 2.21x-8.59x faster than co-scheduled k x BFS on maps with side 256, 512, and 1024 cells.
Open source S5speedup factor
8.59
View original evidence
experiments confirm exact agreement with reference searches on fully built maps and show that dense many-to-many queries at a few dozen viewpoints are up to two orders of magnitude faster than sequential k x BFS. On exploration runs with sixteen robots, in-loop queries on the growing occupancy graph are 2.21x-8.59x faster than co-scheduled k x BFS on maps with side 256, 512, and 1024 cells.
Open source S5map side length
256
View original evidence
experiments confirm exact agreement with reference searches on fully built maps and show that dense many-to-many queries at a few dozen viewpoints are up to two orders of magnitude faster than sequential k x BFS. On exploration runs with sixteen robots, in-loop queries on the growing occupancy graph are 2.21x-8.59x faster than co-scheduled k x BFS on maps with side 256, 512, and 1024 cells.
Open source S5map side length
512
View original evidence
experiments confirm exact agreement with reference searches on fully built maps and show that dense many-to-many queries at a few dozen viewpoints are up to two orders of magnitude faster than sequential k x BFS. On exploration runs with sixteen robots, in-loop queries on the growing occupancy graph are 2.21x-8.59x faster than co-scheduled k x BFS on maps with side 256, 512, and 1024 cells.
Open source S5map side length
1,024
View original evidence
experiments confirm exact agreement with reference searches on fully built maps and show that dense many-to-many queries at a few dozen viewpoints are up to two orders of magnitude faster than sequential k x BFS. On exploration runs with sixteen robots, in-loop queries on the growing occupancy graph are 2.21x-8.59x faster than co-scheduled k x BFS on maps with side 256, 512, and 1024 cells.
Open source S5
- paper: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
experiments confirm exact agreement with reference searches on fully built maps and show that dense many-to-many queries at a few dozen viewpoints are up to two orders of magnitude faster than sequential k x BFS. On exploration runs with sixteen robots, in-loop queries on the growing occupancy graph are 2.21x-8.59x faster than co-scheduled k x BFS on maps with side 256, 512, and 1024 cells.
Open source S5
Source：arXiv Robotics — research abstracts · arxiv.org