arXiv:1702.05515
Overview: Generalizations of Multi-Agent Path Finding to Real-World Scenarios
원문 초록
Multi-agent path finding (MAPF) is well-studied in artificial intelligence, robotics, theoretical computer science and operations research. We discuss issues that arise when generalizing MAPF methods to real-world scenarios and four research directions that address them. We emphasize the importance of addressing these issues as opposed to developing faster methods for the standard formulation of the MAPF problem.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Accelerating Focal Search in Multi-Agent Path Finding with Tighter Lower Bounds
SIGMA: Sheaf-Informed Geometric Multi-Agent Pathfinding
Simultaneous Computation with Multiple Prioritizations in Multi-Agent Motion Planning
Graph Coloring to Reduce Computation Time in Prioritized Planning
Multi-Agent Motion Planning For Differential Drive Robots Through Stationary State Search
Loosely Synchronized Rule-Based Planning for Multi-Agent Path Finding with Asynchronous Actions
토론
이 논문과 연결된 포럼 글이 아직 없습니다.