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..
Hierarchical Model Predictive Control for Multi-Robot Navigation
Authors: Chao Huang, Xin Chen, Yifan Zhang, Shengchao Qin, Yifeng Zeng, Xuandong Li
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed control scheme guarantees the stability and feasibility, and is more efficient and viable than other Model Predictive Control schemes, as evidenced by our simulation results. |
| Researcher Affiliation | Academia | 1State Key Laboratory for Novel Software Technology, Nanjing University, China 2School of Computing, Teesside University, UK 3Shenzhen University, China |
| Pseudocode | No | The HMPC scheme steps are described in text, but no formally labeled "Pseudocode" or "Algorithm" block is present. |
| Open Source Code | No | The paper mentions using "CVX a Matlab-based package" and "Multi-Parametric Toolbox 3.0" but does not provide specific access to its own implementation code for HMPC. |
| Open Datasets | No | The paper describes simulation scenarios ("stationary formation" and "moving aggregation") with robots and their dynamics, but it does not use a publicly available or open dataset, nor does it provide access information for the data generated in its simulations. |
| Dataset Splits | No | The paper describes simulation scenarios and parameters but does not involve machine learning datasets with explicit training, validation, or test splits. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, memory) used for running the simulations. |
| Software Dependencies | Yes | We use the CVX a Matlab-based package [CVX Research, 2012; Grant and Boyd, 2008] for solving optimization problems in multiple MPC schemes. ... computed by the multi-parametric toolbox [Herceg et al., 2013], which is the Ki-step reachable set from the origin, i.e. Ri(0, Ki). |
| Experiment Setup | Yes | The parameter setting in the simulation is as follows. First, the sampling interval of i-th robot is defined:... The input constraint of the i-th robot is: Ui = {u : |u| ≤ 0.2}. The safety distance is dsafe = 0.6. In practice, most works [Rawlings and Muske, 1993; Sznaier and Damborg, 1987] advocate choosing the horizon H online or big enough so that the optimal state q(H|t) obtained by solving problem (5) without the terminal constraint actually satisfies q(H|t) ∈ Qf. Also, a large enough weight of l H can ensure Assumption A4 to be met. Accordingly we give the configuration of parameters to make CMPC stable (hence, so is our HMPC): l H(q) = 10lq(q); Qf = R4; H = 9; p = 2 in the cost function, i.e. l is quadratic. Other parameters will be chosen differently for each scenario. ... Weighting factors in the cost function are chosen to be Cabs = Crel = I4, Cu = 0.01I2. ... setting the weighting factor of absolute position to 0: Cabs = 0, Crel = I4, Cu = 0.01I2. |