26 citations · 54 across the 12 of their papers we have counts for
12 papers
New Mechanisms in Flex Distribution for Bounded Suboptimal Multi-Agent Path Finding
Shao-Hung Chan, Thomy Phan, Jiaoyang Li +1
Multi-Agent Path Finding (MAPF) is the problem of finding a set of collision-free paths, one for each agent in a shared environment. Its objective is to minimize the sum of path co…
Accelerating Focal Search in Multi-Agent Path Finding with Tighter Lower Bounds
Yimin Tang, Zhenghong Yu, Jiaoyang Li +1
Multi-Agent Path Finding (MAPF) involves finding collision-free paths for multiple agents while minimizing a cost function--an NP-hard problem. Bounded suboptimal methods like Enha…
RAILGUN: A Unified Convolutional Policy for Multi-Agent Path Finding Across Different Environments and Tasks
Yimin Tang, Xiao Xiong, Jingyi Xi +3
Multi-Agent Path Finding (MAPF), which focuses on finding collision-free paths for multiple robots, is crucial for applications ranging from aerial swarms to warehouse automation.…
Enhancing Lifelong Multi-Agent Path Finding with Cache Mechanism
Yimin Tang, Zhenghong Yu, Yi Zheng +3
Multi-Agent Path Finding (MAPF), which focuses on finding collision-free paths for multiple robots, is crucial in autonomous warehouse operations. Lifelong MAPF (L-MAPF), where age…
ITA-ECBS: A Bounded-Suboptimal Algorithm for the Combined Target-Assignment and Path-Finding Problem
Yimin Tang, Sven Koenig, Jiaoyang Li
Multi-Agent Path Finding (MAPF), i.e., finding collision-free paths for multiple robots, plays a critical role in many applications. Sometimes, assigning a target to each agent als…
Caching-Augmented Lifelong Multi-Agent Path Finding
Yimin Tang, Zhenghong Yu, Yi Zheng +3
Multi-Agent Path Finding (MAPF), which involves finding collision-free paths for multiple robots, is crucial in various applications. Lifelong MAPF, where targets are reassigned to…
No Panacea in Planning: Algorithm Selection for Suboptimal Multi-Agent Path Finding
Weizhe Chen, Zhihan Wang, Jiaoyang Li +2
Since more and more algorithms are proposed for multi-agent path finding (MAPF) and each of them has its strengths, choosing the correct one for a specific scenario that fulfills s…
Multi-Robot Geometric Task-and-Motion Planning for Collaborative Manipulation Tasks
Hejia Zhang, Shao-Hung Chan, Jie Zhong +6
We address multi-robot geometric task-and-motion planning (MR-GTAMP) problems in synchronous, monotone setups. The goal of the MR-GTAMP problem is to move objects with multiple rob…
Cooperative Task and Motion Planning for Multi-Arm Assembly Systems
Jingkai Chen, Jiaoyang Li, Yijiang Huang +7
Multi-robot assembly systems are becoming increasingly appealing in manufacturing due to their ability to automatically, flexibly, and quickly construct desired structural designs.…
Flatland Competition 2020: MAPF and MARL for Efficient Train Coordination on a Grid World
Florian Laurent, Manuel Schneider, Christian Scheller +24
The Flatland competition aimed at finding novel approaches to solve the vehicle re-scheduling problem (VRSP). The VRSP is concerned with scheduling trips in traffic networks and th…
Multi-Robot Routing with Time Windows: A Column Generation Approach
Naveed Haghani, Jiaoyang Li, Sven Koenig +4
Robots performing tasks in warehouses provide the first example of wide-spread adoption of autonomous vehicles in transportation and logistics. The efficiency of these operations,…
Pairwise Symmetry Reasoning for Multi-Agent Path Finding Search
Jiaoyang Li, Daniel Harabor, Peter J. Stuckey +1
Multi-Agent Path Finding (MAPF) is a challenging combinatorial problem that asks us to plan collision-free paths for a team of cooperative agents. In this work, we show that one of…