MORRF*: Sampling-Based Multi-Objective Motion Planning
Authors: Daqing Yi, Michael A. Goodrich, Kevin D Seppi
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | 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 daqing.yi@byu.edu Michael A. Goodrich Computer Science Dept. Brigham Young University Provo, UT 84602, USA mike@cs.byu.edu Kevin D. Seppi Computer Science Dept. Brigham Young University Provo, UT 84602, USA kseppi@cs.byu.edu |
| 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}. |