Summary of Pathfinding with Lazy Successor Generation, by Keisuke Okumura
Pathfinding with Lazy Successor Generation
by Keisuke Okumura
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Robotics (cs.RO)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed LaCAS* algorithm effectively solves a challenging pathfinding problem by leveraging an oracle that provides connectivity information between locations. The traditional approach to this problem is hindered by its massive branching factor, making it difficult for search algorithms to complete searches efficiently. To mitigate this issue, the authors develop a novel algorithm that gradually generates successors as the search progresses, utilizing k-nearest neighbors search on a k-d tree. This anytime algorithm ensures completeness and eventually converges to optimal solutions. Experimental results demonstrate LaCAS*’s efficacy in solving complex pathfinding instances quickly, outperforming conventional methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed an innovative way to find the shortest path between two locations when all we know are the locations themselves, not the paths connecting them. This problem is tricky because it requires searching through a huge number of possible connections, making it hard for computers to find the right solution. The new algorithm, called LaCAS, gets around this problem by gradually building up the search as it goes along. It uses a special kind of map called a k-d tree to help narrow down the possibilities. LaCAS is able to solve very complex problems quickly and efficiently, making it useful for all sorts of applications. |
