Optimizing and predicting swarming collective motion performance for coverage problems solving: A simulation-optimization approach
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| Název: | Optimizing and predicting swarming collective motion performance for coverage problems solving: A simulation-optimization approach |
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| Autoři: | Ghanem, R, Ali, IM, Abpeikar, S, Kasmarik, K, Garratt, M |
| Zdroj: | urn:ISSN:0952-1976 ; urn:ISSN:1873-6769 ; Engineering Applications of Artificial Intelligence, 139, 109522 |
| Informace o vydavateli: | Elsevier |
| Rok vydání: | 2025 |
| Sbírka: | UNSW Sydney (The University of New South Wales): UNSWorks |
| Témata: | 4605 Data Management and Data Science, 46 Information and Computing Sciences, 4602 Artificial Intelligence, anzsrc-for: 4605 Data Management and Data Science, anzsrc-for: 46 Information and Computing Sciences, anzsrc-for: 4602 Artificial Intelligence, anzsrc-for: 08 Information and Computing Sciences, anzsrc-for: 09 Engineering, anzsrc-for: 40 Engineering |
| Popis: | Algorithms using swarming collective motion can solve coverage problems in unknown environments by reacting to unknown obstacles in real-time when they are encountered. However, these algorithms face two key challenges when deployed on real robots. First, hand-tuning efficient collective motion parameters is both time-consuming and difficult. Second, predicting the time required for a swarm to solve a particular problem is not straightforward. This paper introduces a novel evolutionary framework to address both problems by proposing a methodology that autonomously tunes collective motion parameters for coverage problems while predicting the time required for real robots to complete the task. Our approach utilizes a simulation–optimization framework that employs a genetic algorithm to optimize the parameters of a frontier-led swarming algorithm. Results indicate that the optimized parameters are transferable to real robots, achieving 100% coverage while maintaining 84% connectivity between them. Compared to state-of-the-art swarm methods, our system reduced turnaround time by 50% and 57% in different environments while maintaining collective motion. It also achieved a 55% reduction in turnaround time on average across five scenarios compared to budget-constrained path planning, with a 10% increase in coverage. Furthermore, our framework outperformed both hand-tuned and learned collective motion approaches, reducing turnaround time by 73% in non-collective motion scenarios and by 63% while maintaining 85% connectivity in collective motion scenarios. This approach effectively combines the adaptability of swarm behavior with the predictive reliability of planning methods. |
| Druh dokumentu: | article in journal/newspaper |
| Jazyk: | unknown |
| Relation: | https://hdl.handle.net/1959.4/105764; https://doi.org/10.1016/j.engappai.2024.109522 |
| DOI: | 10.1016/j.engappai.2024.109522 |
| Dostupnost: | https://hdl.handle.net/1959.4/105764 https://doi.org/10.1016/j.engappai.2024.109522 |
| Rights: | open access ; https://purl.org/coar/access_right/c_abf2 ; CC-BY ; https://creativecommons.org/licenses/by/4.0/ |
| Přístupové číslo: | edsbas.E195C58B |
| Databáze: | BASE |
Nájsť tento článok vo Web of Science | Abstrakt: | Algorithms using swarming collective motion can solve coverage problems in unknown environments by reacting to unknown obstacles in real-time when they are encountered. However, these algorithms face two key challenges when deployed on real robots. First, hand-tuning efficient collective motion parameters is both time-consuming and difficult. Second, predicting the time required for a swarm to solve a particular problem is not straightforward. This paper introduces a novel evolutionary framework to address both problems by proposing a methodology that autonomously tunes collective motion parameters for coverage problems while predicting the time required for real robots to complete the task. Our approach utilizes a simulation–optimization framework that employs a genetic algorithm to optimize the parameters of a frontier-led swarming algorithm. Results indicate that the optimized parameters are transferable to real robots, achieving 100% coverage while maintaining 84% connectivity between them. Compared to state-of-the-art swarm methods, our system reduced turnaround time by 50% and 57% in different environments while maintaining collective motion. It also achieved a 55% reduction in turnaround time on average across five scenarios compared to budget-constrained path planning, with a 10% increase in coverage. Furthermore, our framework outperformed both hand-tuned and learned collective motion approaches, reducing turnaround time by 73% in non-collective motion scenarios and by 63% while maintaining 85% connectivity in collective motion scenarios. This approach effectively combines the adaptability of swarm behavior with the predictive reliability of planning methods. |
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| DOI: | 10.1016/j.engappai.2024.109522 |