Large-Scale Multi-Robot Coverage Path Planning via Local Search
Authors: Jingtao Tang, Hang Ma
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments demonstrate the effectiveness of LS-MCPP, consistently improving the initial solution returned by two state-of-the-art baseline algorithms that compute suboptimal tree covers on G, with a notable reduction in makespan by up to 35.7% and 30.3%, respectively. To validate the effectiveness of LS-MCPP, we conduct extensive experiments, comparing it with three state-of-the-art baseline graph-based MCPP algorithms that operate on complete terrain graphs only. |
| Researcher Affiliation | Academia | Jingtao Tang, Hang Ma Simon Fraser University {jingtao tang, hangma}@sfu.ca |
| Pseudocode | Yes | Algorithm 1: LS-MCPP |
| Open Source Code | Yes | Code: https://github.com/reso1/LS-MCPP |
| Open Datasets | Yes | We use MCPP instances from (Tang and Ma 2023), where their numbers of graph vertices, graph edges, and robots range from 46 to 824, 60 to 1495, and 4 to 12, respectively. As shown in Fig. 6 and Tab. 1, we design three additional larger instances whose terrain graphs are adopted from popular 2D pathfinding benchmarks (Sturtevant 2012). |
| Dataset Splits | No | The paper uses specific MCPP instances for evaluation but does not provide details on training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | Yes | This section describes our experimental results on an Intel Xeon Gold 5218 2.30 GHz Linux server with 300 GB memory. |
| Software Dependencies | No | The paper does not provide specific version numbers for software components or libraries used in the experiments. |
| Experiment Setup | Yes | For all instances solved with LS-MCPP, the pool weight decay factor γ is set to 0.01, and the temperature decay factor α is set to exp log 0.2 M to decrease the temperature t from 1 to 0.2. For instances from (Tang and Ma 2023), we set the max iteration M to 3e3 and the forced deduplication step S to 1e2, For the three additional larger instances in Tab. 1, we set M to 1.5e4 and S to 5e2. |