CODECHECK Certificate 2025-019
Paper details
Authors: Filip Surma, Anahita Jamshidnejad
Abstract: Obtained from OpenAlex
This paper introduces a novel concept, fuzzy-logic-based model predictive control (FLMPC), along with a multi-robot control approach for exploring unknown environments and locating targets. Traditional model predictive control (MPC) methods rely on Bayesian theory to represent environmental knowledge and optimize a stochastic cost function, often leading to high computational costs and lack of effectiveness in locating all the targets. Our approach instead leverages FLMPC and extends it to a bi-level parent-child architecture for enhanced coordination and extended decision making horizon. Extracting high-level information from probability distributions and local observations, FLMPC simplifies the optimization problem and significantly extends its operational horizon compared to other MPC methods. We conducted extensive simulations in unknown 2-dimensional environments with randomly placed obstacles and humans. We compared the performance and computation time of FLMPC against MPC with a stochastic cost function, then evaluated the impact of integrating the high-level parent FLMPC layer. The results indicate that our approaches significantly improve both performance and computation time, enhancing coordination of robots and reducing the impact of uncertainty in large-scale search and rescue environments.
CODECHECK details
Certificate identifier: 2025-019
Codechecker name: Joao Guimaraes
Time of check: 2025-06-11 10:00:00
Repository: https://github.com/codecheckers/certificate-2025-019
Full certificate: https://doi.org/10.5281/zenodo.15771677
Summary:
Figures 3-8 from the manuscript were successfully reproduced. Figures 1-2 and Tables 1-3 were not covered by this CODECHECK. To avoid the lengthy execution time required to run the full experiments, the pre-computed results used to generate the figures were provided in a separate repository.
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