Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
MORRF*: Sampling-Based Multi-Objective Motion Planning
Authors: Daqing Yi, Michael A. Goodrich, Kevin D Seppi
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now present a series of simulation studies that provide evidence that MORRF produces a representative set of samples from the Pareto set. Results from MORRF are obtained for path-planning problems with two objectives and three objectives, and are compared to a modified version of the NSGA-II multi-objective path-planning algorithm [Ahmed and Deb, 2013] as well as a variant of MORRF that uses a weighted sum rather than the Tchebycheff approach. |
| Researcher Affiliation | Academia | Daqing Yi Computer Science Dept. Brigham Young University Provo, UT 84602, USA EMAIL Michael A. Goodrich Computer Science Dept. Brigham Young University Provo, UT 84602, USA EMAIL Kevin D. Seppi Computer Science Dept. Brigham Young University Provo, UT 84602, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 Multi-Objective Rapidly exploring Random Forest; Algorithm 2 EXTENDRef (G, xnew, xnearest, k); Algorithm 3 EXTENDSub (G, xnew, xnearest, m) |
| Open Source Code | No | The paper does not provide information about open-source code for the described methodology. |
| Open Datasets | No | The paper describes simulated environments ('obstacle-free world', 'environment with obstacles') for path planning, and defines abstract problem spaces (e.g., 'X Rd', 'Xobs'), but it does not explicitly use or refer to a named, publicly available dataset with concrete access information (e.g., URL, DOI, specific citation to an established benchmark). |
| Dataset Splits | No | The paper does not explicitly provide information on training, validation, or test dataset splits, as its simulations appear to be conducted on generated environments rather than using pre-defined dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions algorithms like NSGA-II and MOEA-D, but it does not provide specific software names with version numbers or reproducible details for ancillary software dependencies used in its implementation or experiments. |
| Experiment Setup | Yes | Each method was run for 5000 iterations and restricted to 30 solutions. STEER(): Given two points x and y, returns a point z on the line segment from x to y that that is no greater than η from y. NEAR(G, x, η): Returns a set of all vertices within the closed ball of radius rn centered at x, in which rn = min{( γ n )1/d, η}. |