Multi-robot Task Allocation in the Environment with Functional Tasks
Authors: Fuhan Yan, Kai Di
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The simulated experiments demonstrate that the heuristic algorithm can outperform the benchmark algorithms. |
| Researcher Affiliation | Academia | 1College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China. 2School of computer science and engineering, Southeast University, Nanjing, China. |
| Pseudocode | Yes | Algorithm 1 Greedy task allocation (fast) Input: Task sequences S1, S2. .. S|R| based on SSI Output: S1, S2. .. S|R| |
| Open Source Code | No | The paper does not provide concrete access to source code (e.g., a specific repository link or an explicit code release statement). |
| Open Datasets | No | The paper describes a simulated environment where tasks are "randomly uniformly distributed". It does not provide access information (link, DOI, citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper uses a simulated environment and describes parameters for data generation (e.g., "XY = 100 100", "All tasks are randomly uniformly distributed"). It does not provide specific training/test/validation dataset splits, as it's a simulation setup rather than working with pre-existing datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Some parameters are fixed: XY = 100 100; |Tc| = 40; fi is randomly set in [1,5]; νi is randomly set in [1, νmax]; g(T j f ) = 1 1+ln Q tk T j f fk . All tasks are randomly uniformly distributed in the grid world. Each experiment is performed with 1000 replications, and the average data are shown in the experimental figures. In the experiments, the two parameters are set heuristically: TS = 0.5, γ = 0.5. |