Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.21065 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912296570191872 |
|---|---|
| author | Surma, Filip Jamshidnejad, Anahita |
| author_facet | Surma, Filip Jamshidnejad, Anahita |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_21065 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fuzzy-Logic-based model predictive control: A paradigm integrating optimal and common-sense decision making Surma, Filip Jamshidnejad, Anahita Robotics Optimization and Control 93B45, 93C42 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. |
| title | Fuzzy-Logic-based model predictive control: A paradigm integrating optimal and common-sense decision making |
| topic | Robotics Optimization and Control 93B45, 93C42 |
| url | https://arxiv.org/abs/2503.21065 |