Summary of Optimal and Bounded Suboptimal Any-angle Multi-agent Pathfinding, by Konstantin Yakovlev et al.
Optimal and Bounded Suboptimal Any-Angle Multi-agent Pathfinding
by Konstantin Yakovlev, Anton Andreychuk, Roni Stern
First submitted to arxiv on: 25 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Multiagent Systems (cs.MA); 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 paper presents an optimal algorithm for multi-agent pathfinding (MAPF) when agents can move between any pair of possible locations as long as traversing the line segment connecting them does not lead to a collision with obstacles. The algorithm, based on Continuous Conflict-based Search (CCBS) and Safe Interval Path Planning (TO-AA-SIPP), is designed to solve MAPF problems efficiently despite the increased complexity introduced by any-angle pathfinding. To mitigate the scalability issues, the authors adapt two techniques from classical MAPF, Disjoint Splitting and Multi-Constraints, to the any-angle setting. Experimental results show that these adaptations enable solving over 30% more problems than the vanilla combination of CCBS and TO-AA-SIPP, while also presenting a bounded-suboptimal variant for controlled trade-offs between runtime and solution cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a complex problem called multi-agent pathfinding (MAPF) in a new way. Normally, agents can only move to certain nearby places on a grid, but this algorithm lets them move between any two points as long as they don’t crash into obstacles. The authors use special search algorithms and techniques to make it efficient. They tested their method and found that it could solve more problems than other methods, and they also came up with a way to balance how fast the algorithm runs versus how good its solution is. |
