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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Maintaining Communication in Multi-Robot Tree Coverage
Authors: Mor Sinay, Noa Agmon, Oleg Maksimov, Sarit Kraus, David Peleg
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We enhance the theoretically-proven solution with a dripping heuristic algorithm, and show in extensive simulations that it significantly decreases the coverage time. The algorithm is then adjusted to general (not necessarily perfect) N-ary trees and additional experiments prove its efficiency. Furthermore, we show the use of our solution in a simulated officebuilding scenario. Finally, we deploy our algorithm on real robots in a real office building setting, showing efficient coverage time in practice. |
| Researcher Affiliation | Academia | 1Bar-Ilan University, Israel 2 The Weizmann Institute, Israel |
| Pseudocode | Yes | Algorithm 1 NCOCTA; Algorithm 2 COCTA |
| Open Source Code | No | The paper mentions implementing solutions on ROS/Gazebo, but does not provide a link or statement about releasing their specific implementation code. |
| Open Datasets | No | The paper uses simulations and real-world deployments in custom environments (N-ary trees, simulated office building) rather than named public datasets with concrete access information. |
| Dataset Splits | No | The paper describes experiments on simulated and real-world environments but does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper mentions using 'Hamster robots' for real-world deployment but does not provide specific hardware specifications like GPU/CPU models or memory details for these robots or for the simulation environment. |
| Software Dependencies | Yes | We have implemented our solutions on ROS/Gazebo 1. |
| Experiment Setup | Yes | Simulation of NCOCTA algorithm on a perfect 2-ary tree when k = 11, H = 3 and h = {3, 2}. ... SF vs number of robots on a perfect 2-ary tree (H=15), 3-ary tree (H=8), 4-ary tree (H=8). ... SF on a perfect 2-ary tree using 60 robots with different tree heights. ... In order to create these imperfect trees, we defined a number of nodes to remove from the tree, and removed them from a predefined height (and all its subtrees) at random. |