32 citations · 72 across the 24 of their papers we have counts for
11 papers · 1 filter
Planning over MAPF Agent Dependencies via Multi-Dependency PIBT
Zixiang Jiang, Yulun Zhang, Rishi Veerapaneni +1
Modern Multi-Agent Path Finding (MAPF) algorithms must plan for hundreds to thousands of agents in congested environments within a second, requiring highly efficient algorithms. Pr…
Conflict-Based Search as a Protocol: A Multi-Agent Motion Planning Protocol for Heterogeneous Agents, Solvers, and Independent Tasks
Rishi Veerapaneni, Alvin Tang, Haodong He +9
Imagine the future construction site, hospital, or office with dozens of robots bought from different manufacturers. How can we enable these different robots to effectively move in…
Dynamic Agent Grouping ECBS: Scaling Windowed Multi-Agent Path Finding with Completeness Guarantees
Tiannan Zhang, Rishi Veerapaneni, Shao-Hung Chan +2
Multi-Agent Path Finding (MAPF) is the problem of finding a set of collision-free paths for a team of agents. Although several MAPF methods which solve full-horizon MAPF have compl…
BTPG-max: Achieving Local Maximal Bidirectional Pairs for Bidirectional Temporal Plan Graphs
Yifan Su, Rishi Veerapaneni, Jiaoyang Li
Multi-Agent Path Finding (MAPF) requires computing collision-free paths for multiple agents in shared environment. Most MAPF planners assume that each agent reaches a specific loca…
Real-Time LaCAM for Real-Time MAPF
Runzhe Liang, Rishi Veerapaneni, Daniel Harabor +2
The vast majority of Multi-Agent Path Finding (MAPF) methods with completeness guarantees require planning full-horizon paths. However, planning full-horizon paths can take too lon…
Deploying Ten Thousand Robots: Scalable Imitation Learning for Lifelong Multi-Agent Path Finding
He Jiang, Yutong Wang, Rishi Veerapaneni +3
Lifelong Multi-Agent Path Finding (LMAPF) repeatedly finds collision-free paths for multiple agents that are continually assigned new goals when they reach current ones. Recently,…
Windowed MAPF with Completeness Guarantees
Rishi Veerapaneni, Muhammad Suhail Saleem, Jiaoyang Li +1
Traditional multi-agent path finding (MAPF) methods try to compute entire start-goal paths which are collision free. However, computing an entire path can take too long for MAPF sy…
Work Smarter Not Harder: Simple Imitation Learning with CS-PIBT Outperforms Large Scale Imitation Learning for MAPF
Rishi Veerapaneni, Arthur Jakobsson, Kevin Ren +3
Multi-Agent Path Finding (MAPF) is the problem of effectively finding efficient collision-free paths for a group of agents in a shared workspace. The MAPF community has largely foc…
Scaling Lifelong Multi-Agent Path Finding to More Realistic Settings: Research Challenges and Opportunities
He Jiang, Yulun Zhang, Rishi Veerapaneni +1
Multi-Agent Path Finding (MAPF) is the problem of moving multiple agents from starts to goals without collisions. Lifelong MAPF (LMAPF) extends MAPF by continuously assigning new g…
From Space-Time to Space-Order: Directly Planning a Temporal Planning Graph by Redefining CBS
Yu Wu, Rishi Veerapaneni, Jiaoyang Li +1
The majority of multi-agent path finding (MAPF) methods compute collision-free space-time paths which require agents to be at a specific location at a specific discretized timestep…
Improving Learnt Local MAPF Policies with Heuristic Search
Rishi Veerapaneni, Qian Wang, Kevin Ren +3
Multi-agent path finding (MAPF) is the problem of finding collision-free paths for a team of agents to reach their goal locations. State-of-the-art classical MAPF solvers typically…