Lifelong Multi-Agent Path Finding in Large-Scale Warehouses
Authors: Jiaoyang Li, Andrew Tinka, Scott Kiesel, Joseph W. Durham, T. K. Satish Kumar, Sven Koenig11272-11281
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically evaluate RHCR with a variety of MAPF solvers and show that it can produce high-quality solutions for up to 1,000 agents (= 38.9% of the empty cells on the map) for simulated warehouse instances, significantly outperforming existing work. |
| Researcher Affiliation | Collaboration | Jiaoyang Li,1 Andrew Tinka,2 Scott Kiesel,2 Joseph W. Durham,2 T. K. Satish Kumar,1 Sven Koenig1 1 University of Southern California 2 Amazon Robotics |
| Pseudocode | Yes | Algorithm 1: The low-level search for Windowed MAPF solvers generalizing Multi-Label A* (Grenouilleau, van Hoeve, and Hooker 2019). |
| Open Source Code | Yes | The code is available at https://github.com/Jiaoyang-Li/RHCR. |
| Open Datasets | Yes | We use the map in Figure 3a from (Liu et al. 2019). ... We use the map in Figure 3b. |
| Dataset Splits | No | The paper simulates environments and assigns tasks dynamically, but it does not specify explicit training, validation, or test dataset splits in the traditional sense. |
| Hardware Specification | Yes | We conduct all experiments on Amazon EC2 instances of type m4.xlarge with 16 GB memory. |
| Software Dependencies | No | The paper mentions software components like C++, SIPP, and SCIPP, and specific MAPF solvers (CBS, ECBS, CA*, PBS), but it does not provide version numbers for any of these dependencies. |
| Experiment Setup | Yes | For RHCR, we use time horizon w = 20 and replanning period h = 5. ... We simulate 5,000 timesteps for each experiment with potential function threshold p = 1. ... ECBS with suboptimality factor 1.1, CA* with random restarts. |