Reda Ghanem , Ismail M. Ali , Shadi Abpeikar , Kathryn Kasmarik , Matthew Garratt
{"title":"Optimizing and predicting swarming collective motion performance for coverage problems solving: A simulation-optimization approach","authors":"Reda Ghanem , Ismail M. Ali , Shadi Abpeikar , Kathryn Kasmarik , Matthew Garratt","doi":"10.1016/j.engappai.2024.109522","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5000,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Applications of Artificial Intelligence","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0952197624016804","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.