Data Fusion Applied to the Leader-Based Bat Algorithm to Improve the Localization of Mobile Robots
The increasing demand for autonomous mobile robots in complex environments calls for efficient path-planning algorithms. Bio-inspired algorithms effectively address intricate optimization challenges, but their computational cost increases with the number of particles, which is great when implementin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/403 |
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| author | Wolmar Araujo-Neto Leonardo Rocha Olivi Daniel Khede Dourado Villa Mário Sarcinelli-Filho |
| author_facet | Wolmar Araujo-Neto Leonardo Rocha Olivi Daniel Khede Dourado Villa Mário Sarcinelli-Filho |
| author_sort | Wolmar Araujo-Neto |
| collection | DOAJ |
| description | The increasing demand for autonomous mobile robots in complex environments calls for efficient path-planning algorithms. Bio-inspired algorithms effectively address intricate optimization challenges, but their computational cost increases with the number of particles, which is great when implementing algorithms of high accuracy. To address such topics, this paper explores the application of the leader-based bat algorithm (LBBA), an enhancement of the traditional bat algorithm (BA). By dynamically incorporating robot orientation as a guiding factor in swarm distribution, LBBA improves mobile robot localization. A digital compass provides precise orientation feedback, promoting better particle distribution, thus reducing computational overhead. Experiments were conducted using a mobile robot in controlled environments containing obstacles distributed in diverse configurations. Comparative studies with leading algorithms, such as Manta Ray Foraging Optimization (MRFO) and Black Widow Optimization (BWO), highlighted the proposed algorithm’s ability to achieve greater path accuracy and faster convergence, even when using fewer particles. The algorithm consistently demonstrated robustness in bypassing local minima, a notable limitation of conventional bio-inspired approaches. Therefore, the proposed algorithm is a promising solution for real-time localization in resource-constrained environments, enhancing the accuracy and efficiency in the guidance of mobile robots, thus highlighting its potential for broader adoption in mobile robotics. |
| format | Article |
| id | doaj-art-5d28d6e8a38d4756b64adfe6bae49f89 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-5d28d6e8a38d4756b64adfe6bae49f892025-01-24T13:48:48ZengMDPI AGSensors1424-82202025-01-0125240310.3390/s25020403Data Fusion Applied to the Leader-Based Bat Algorithm to Improve the Localization of Mobile RobotsWolmar Araujo-Neto0Leonardo Rocha Olivi1Daniel Khede Dourado Villa2Mário Sarcinelli-Filho3Department of Electrical Engineering, Universidade Federal do Espírito Santo, Vitória 29075-910, ES, BrazilDepartment of Electrical Energy, Universidade Federal de Juiz de Fora, Juiz de Fora 36036-900, MG, BrazilDepartment of Electrical Engineering, Universidade Federal do Espírito Santo, Vitória 29075-910, ES, BrazilDepartment of Electrical Engineering, Universidade Federal do Espírito Santo, Vitória 29075-910, ES, BrazilThe increasing demand for autonomous mobile robots in complex environments calls for efficient path-planning algorithms. Bio-inspired algorithms effectively address intricate optimization challenges, but their computational cost increases with the number of particles, which is great when implementing algorithms of high accuracy. To address such topics, this paper explores the application of the leader-based bat algorithm (LBBA), an enhancement of the traditional bat algorithm (BA). By dynamically incorporating robot orientation as a guiding factor in swarm distribution, LBBA improves mobile robot localization. A digital compass provides precise orientation feedback, promoting better particle distribution, thus reducing computational overhead. Experiments were conducted using a mobile robot in controlled environments containing obstacles distributed in diverse configurations. Comparative studies with leading algorithms, such as Manta Ray Foraging Optimization (MRFO) and Black Widow Optimization (BWO), highlighted the proposed algorithm’s ability to achieve greater path accuracy and faster convergence, even when using fewer particles. The algorithm consistently demonstrated robustness in bypassing local minima, a notable limitation of conventional bio-inspired approaches. Therefore, the proposed algorithm is a promising solution for real-time localization in resource-constrained environments, enhancing the accuracy and efficiency in the guidance of mobile robots, thus highlighting its potential for broader adoption in mobile robotics.https://www.mdpi.com/1424-8220/25/2/403robot localizationoptimizationdata fusionautonomous mobile robots |
| spellingShingle | Wolmar Araujo-Neto Leonardo Rocha Olivi Daniel Khede Dourado Villa Mário Sarcinelli-Filho Data Fusion Applied to the Leader-Based Bat Algorithm to Improve the Localization of Mobile Robots Sensors robot localization optimization data fusion autonomous mobile robots |
| title | Data Fusion Applied to the Leader-Based Bat Algorithm to Improve the Localization of Mobile Robots |
| title_full | Data Fusion Applied to the Leader-Based Bat Algorithm to Improve the Localization of Mobile Robots |
| title_fullStr | Data Fusion Applied to the Leader-Based Bat Algorithm to Improve the Localization of Mobile Robots |
| title_full_unstemmed | Data Fusion Applied to the Leader-Based Bat Algorithm to Improve the Localization of Mobile Robots |
| title_short | Data Fusion Applied to the Leader-Based Bat Algorithm to Improve the Localization of Mobile Robots |
| title_sort | data fusion applied to the leader based bat algorithm to improve the localization of mobile robots |
| topic | robot localization optimization data fusion autonomous mobile robots |
| url | https://www.mdpi.com/1424-8220/25/2/403 |
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