Dynamic Foraging in Swarm Robotics: A Hybrid Approach with Modular Design and Deep Reinforcement Learning Intelligence
This paper proposes a hybrid approach that combines intelligent algorithms and modular design to solve a foraging problem within the context of swarm robotics. Deep reinforcement learning (RL) and particle swarm optimization (PSO) are deployed in the proposed modular architecture. They are utilized...
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| Format: | Article |
| Language: | English |
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Russian Academy of Sciences, St. Petersburg Federal Research Center
2025-01-01
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| Series: | Информатика и автоматизация |
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| Online Access: | https://ia.spcras.ru/index.php/sp/article/view/16312 |
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| author | Ali Hammoud Alaa Iskandar Béla Kovács |
| author_facet | Ali Hammoud Alaa Iskandar Béla Kovács |
| author_sort | Ali Hammoud |
| collection | DOAJ |
| description | This paper proposes a hybrid approach that combines intelligent algorithms and modular design to solve a foraging problem within the context of swarm robotics. Deep reinforcement learning (RL) and particle swarm optimization (PSO) are deployed in the proposed modular architecture. They are utilized to search for many resources that vary in size and exhibit a dynamic nature with unpredictable movements. Additionally, they transport the collected resources to the nest. The swarm comprises 8 E-Puck mobile robots, each equipped with light sensors. The proposed system is built on a 3D environment using the Webots simulator. Through a modular approach, we address complex foraging challenges characterized by a non-static environment and objectives. This architecture enhances manageability, reduces computational demands, and facilitates debugging processes. Our simulations reveal that the RL-based model outperforms PSO in terms of task completion time, efficiency in collecting resources, and adaptability to dynamic environments, including moving targets. Notably, robots equipped with RL demonstrate enhanced individual learning and decision-making abilities, enabling a level of autonomy that fosters collective swarm intelligence. In PSO, the individual behavior of the robots is more heavily influenced by the collective knowledge of the swarm. The findings highlight the effectiveness of a modular design and deep RL for advancing autonomous robotic systems in complex and unpredictable environments. |
| format | Article |
| id | doaj-art-3078e2a4ba444894a04d96e82fce8ae7 |
| institution | Kabale University |
| issn | 2713-3192 2713-3206 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Russian Academy of Sciences, St. Petersburg Federal Research Center |
| record_format | Article |
| series | Информатика и автоматизация |
| spelling | doaj-art-3078e2a4ba444894a04d96e82fce8ae72025-01-21T11:27:25ZengRussian Academy of Sciences, St. Petersburg Federal Research CenterИнформатика и автоматизация2713-31922713-32062025-01-01241517110.15622/ia.24.1.316312Dynamic Foraging in Swarm Robotics: A Hybrid Approach with Modular Design and Deep Reinforcement Learning IntelligenceAli Hammoud0Alaa Iskandar1Béla Kovács2Federal State Budgetary Educational Institution of Higher Education “Kuban State Agrarian University named after I.T. Trubilin”University of MiskolcUniversity of MiskolcThis paper proposes a hybrid approach that combines intelligent algorithms and modular design to solve a foraging problem within the context of swarm robotics. Deep reinforcement learning (RL) and particle swarm optimization (PSO) are deployed in the proposed modular architecture. They are utilized to search for many resources that vary in size and exhibit a dynamic nature with unpredictable movements. Additionally, they transport the collected resources to the nest. The swarm comprises 8 E-Puck mobile robots, each equipped with light sensors. The proposed system is built on a 3D environment using the Webots simulator. Through a modular approach, we address complex foraging challenges characterized by a non-static environment and objectives. This architecture enhances manageability, reduces computational demands, and facilitates debugging processes. Our simulations reveal that the RL-based model outperforms PSO in terms of task completion time, efficiency in collecting resources, and adaptability to dynamic environments, including moving targets. Notably, robots equipped with RL demonstrate enhanced individual learning and decision-making abilities, enabling a level of autonomy that fosters collective swarm intelligence. In PSO, the individual behavior of the robots is more heavily influenced by the collective knowledge of the swarm. The findings highlight the effectiveness of a modular design and deep RL for advancing autonomous robotic systems in complex and unpredictable environments.https://ia.spcras.ru/index.php/sp/article/view/16312swarm roboticsforaging taskmodular designreinforcement learningparticle swarm optimization |
| spellingShingle | Ali Hammoud Alaa Iskandar Béla Kovács Dynamic Foraging in Swarm Robotics: A Hybrid Approach with Modular Design and Deep Reinforcement Learning Intelligence Информатика и автоматизация swarm robotics foraging task modular design reinforcement learning particle swarm optimization |
| title | Dynamic Foraging in Swarm Robotics: A Hybrid Approach with Modular Design and Deep Reinforcement Learning Intelligence |
| title_full | Dynamic Foraging in Swarm Robotics: A Hybrid Approach with Modular Design and Deep Reinforcement Learning Intelligence |
| title_fullStr | Dynamic Foraging in Swarm Robotics: A Hybrid Approach with Modular Design and Deep Reinforcement Learning Intelligence |
| title_full_unstemmed | Dynamic Foraging in Swarm Robotics: A Hybrid Approach with Modular Design and Deep Reinforcement Learning Intelligence |
| title_short | Dynamic Foraging in Swarm Robotics: A Hybrid Approach with Modular Design and Deep Reinforcement Learning Intelligence |
| title_sort | dynamic foraging in swarm robotics a hybrid approach with modular design and deep reinforcement learning intelligence |
| topic | swarm robotics foraging task modular design reinforcement learning particle swarm optimization |
| url | https://ia.spcras.ru/index.php/sp/article/view/16312 |
| work_keys_str_mv | AT alihammoud dynamicforaginginswarmroboticsahybridapproachwithmodulardesignanddeepreinforcementlearningintelligence AT alaaiskandar dynamicforaginginswarmroboticsahybridapproachwithmodulardesignanddeepreinforcementlearningintelligence AT belakovacs dynamicforaginginswarmroboticsahybridapproachwithmodulardesignanddeepreinforcementlearningintelligence |