Optimizing and predicting swarming collective motion performance for coverage problems solving: A simulation-optimization approach

IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Reda Ghanem , Ismail M. Ali , Shadi Abpeikar , Kathryn Kasmarik , Matthew Garratt
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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.
优化和预测蜂群集体运动性能以解决覆盖问题:模拟优化方法
使用蜂群集体运动的算法可以在遇到未知障碍物时实时做出反应,从而解决未知环境中的覆盖问题。然而,这些算法在实际机器人上部署时面临两大挑战。首先,手工调整高效的集体运动参数既耗时又困难。其次,预测机器人群解决特定问题所需的时间并不简单。本文介绍了一种新颖的进化框架,通过提出一种方法来解决这两个问题,该方法可针对覆盖问题自主调整集体运动参数,同时预测真实机器人完成任务所需的时间。我们的方法利用仿真优化框架,采用遗传算法来优化前沿引领的蜂群算法参数。结果表明,优化后的参数可应用于真实机器人,在实现100%覆盖率的同时,还能保持机器人之间84%的连通性。与最先进的蜂群方法相比,我们的系统在不同环境中的周转时间分别缩短了 50%和 57%,同时保持了集体运动。与预算受限的路径规划相比,我们的系统在五种情况下平均减少了 55% 的周转时间,覆盖范围增加了 10%。此外,我们的框架还优于人工调整和学习的集体运动方法,在非集体运动场景下,周转时间缩短了 73%,在集体运动场景下,周转时间缩短了 63%,同时保持了 85% 的连接性。这种方法有效地结合了蜂群行为的适应性和规划方法的预测可靠性。
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来源期刊
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence 工程技术-工程:电子与电气
CiteScore
9.60
自引率
10.00%
发文量
505
审稿时长
68 days
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.
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