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..
Non-Crossing Anonymous MAPF for Tethered Robots
Authors: Xiao Peng, Olivier Simonin, Christine Solnon
JAIR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally evaluate our approach on three different kinds of instances. |
| Researcher Affiliation | Academia | Xiao Peng EMAIL Olivier Simonin EMAIL Christine Solnon EMAIL CITI, INSA Lyon, Inria, F-69621 Villeurbanne, France |
| Pseudocode | Yes | Algorithm 1: Remove Cycle(Π, c) ... Algorithm 2: Build Solution(m, Π) ... Algorithm 3: new VNS(m, Π, kmax) ... Algorithm 4: lazy Approach(ub, Πl) ... Algorithm 5: dichotomous Approach(lb, ub, α) |
| Open Source Code | Yes | Our implementation is publicly available at https://gitlab.inria.fr/xipeng/tethered-amapf-jair2023.git. |
| Open Datasets | No | To study the sensibility of our algorithms to different configurations, we generate instances according to a random model that has three parameters o, n, and d which are described below. For all instances, the bounding polygon is the square B = [0, 200]2. |
| Dataset Splits | No | For each value of n, o, and d, we have randomly generated 30 instances (all instances with a same value of o share the same workspace). |
| Hardware Specification | Yes | All experiments reported in this paper are run on Grid5000 (Balouek, Carpen Amarie, Charrier, Desprez, Jeannot, Jeanvoine, L ebre, Margery, Niclausse, Nussbaum, L., Richard, O., P erez, C., Quesnel, F., Rohr, C., & Sarzyniec, L., 2013) with an AMD EPYC 7642 with 512GB of RAM. |
| Software Dependencies | No | Algorithms have been implemented in Python. In Fig. 8, we display the gap between the optimal makespan opt and the lower bound lb LBAP = maxπi ΠLBAP |πi|, the upper bound ub LSAP = makespan(s LSAP), and the upper bound ub LBAP = makespan(build Solution(s LBAP)). ... Algorithm 4 has been implemented in Java, using the Choco CP library (Prud homme, Fages, & Lorca, 2016). Algorithm 5 has been implemented in Python. |
| Experiment Setup | Yes | For all instances, the value of dt is set to 4 ... k is initialized to 2 and it is incremented each time the current solution is locally optimal; k is reset to 2 each time an improving move has been found; the search is stopped when k exceeds a given upper bound kmax or when a time limit is reached. ... The switching time of 60 seconds is chosen to find a compromise between the quality of the solution and the time required for resolution. ... For dicho, the rate α is set to 0.05 |