Quick Multi-Robot Motion Planning by Combining Sampling and Search
Authors: Keisuke Okumura, Xavier Défago
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical evaluations in various scenarios demonstrate that SSSP significantly outperforms standard approaches to MRMP, i.e., solving more problem instances much faster. |
| Researcher Affiliation | Academia | Keisuke Okumura1,2 , Xavier D efago3 1National Institute of Advanced Industrial Science and Technology (AIST) 2University of Cambridge 3Tokyo Institute of Technology |
| Pseudocode | Yes | Algorithm 1 presents the pseudocode of SSSP. |
| Open Source Code | Yes | The appendix, code, and video are available at https://kei18.github.io/sssp/. |
| Open Datasets | No | For each scenario, we prepared 100 instances by randomly generating initial/goal configurations and obstacle layouts. The paper describes how instances were generated but does not provide concrete access information for a publicly available dataset. |
| Dataset Splits | No | The paper states 'For each scenario, we prepared 100 instances by randomly generating initial/goal configurations and obstacle layouts.' and 'tested each method with 10 different random seeds for each instance (1,000 trials in total).' It describes problem instances for algorithm evaluation but does not specify training, validation, or test dataset splits in the context of machine learning model training. |
| Hardware Specification | Yes | The experiments were run on a desktop PC with Intel Core i9-7960X 2.8 GHz CPU and 64 GB RAM. |
| Software Dependencies | No | The paper states, 'The simulator and all methods were coded in Julia,' but does not provide specific version numbers for Julia or any other software dependencies. |
| Experiment Setup | Yes | To guarantee completeness, SSSP also uses vanilla random sampling with a small probability λ (0.01 in our experiments). |