Effective Integration of Weighted Cost-to-Go and Conflict Heuristic within Suboptimal CBS
Authors: Rishi Veerapaneni, Tushar Kusnur, Maxim Likhachev
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental Results We test our methods with different numbers of agents, in increments of 50, on 8 diverse maps from Stern et al. (2019) and report the mean values across 5 seeds. |
| Researcher Affiliation | Academia | Rishi Veerapaneni, Tushar Kusnur, Maxim Likhachev Robotics Institute, Carnegie Mellon University {rveerapa, tkusnur, mlikhach}@andrew.cmu.edu |
| Pseudocode | Yes | Algorithm 1: Suboptimal CBS low level focal search planner Input: nstart, at Goal(), Paths PI of other agents Output: Lower bound LB on optimal path cost, Path from nstart with sub-optimality wso (i.e. cost wso LB) |
| Open Source Code | No | The paper mentions a detailed version with supplementary material at an arXiv link (https://arxiv.org/abs/2205.11624), but does not explicitly state that its own source code is provided there or elsewhere. |
| Open Datasets | Yes | We test our methods with different numbers of agents, in increments of 50, on 8 diverse maps from Stern et al. (2019) |
| Dataset Splits | No | The paper tests on '8 diverse maps' and reports 'mean values across 5 seeds' but does not specify explicit training/validation/test dataset splits or cross-validation methodology. |
| Hardware Specification | No | The paper states 'The speed up Smethod = Tbaseline/Tmethod is reported to normalize differences in hardware' but does not provide any specific hardware details such as CPU, GPU models, or memory. |
| Software Dependencies | No | The paper refers to using EECBS (Li, Ruml, and Koenig 2021) and its open-source codebase, but it does not specify any particular software dependencies with version numbers (e.g., Python version, library versions) used for its own implementation or experiments. |
| Experiment Setup | Yes | We use wso = 2 and a timeout of 300 seconds in all our experiments unless otherwise specified. In all figures, if a method failed (timed out on all 5 seeds) on a particle number of agents on a map, we do not report larger number of agents. |