Morphology Transformation of Underwater Self-Reconfigurable Modular Robots via Heterogeneous Decomposition and Distributed Control

IF 6.4 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Automation Science and Engineering Pub Date : 2025-01-13 DOI:10.1109/TASE.2025.3528757
Wenjie Lu;Manman Hu
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Abstract

This paper addresses the morphology transformation problem of an underwater self-reconfigurable modular robotic system. Morphology decomposition and reconnections are reduced to mitigate transformation failures and the overhead of underwater wireless communication, giving rise to subgraph matching problems. We propose an efficient probabilistic decomposition method by constraining the search depth of maximal common subgraphs of the initial and goal morphologies. The computational complexity reduces from $O(n^{2})$ to $O(n)$ . The decomposition yields a swarm of heterogeneous clusters, which are interconnected modular robots of varying quantities. The heterogeneity makes the exchange of clusters’ designated positions in the goal morphology not immediately feasible. Subsequently, we present Distributed Control with minimal In-situ task Refinement (DCIR). DCIR is proven to ensure collision-free and deadlock-free morphology transformation. The numerical simulations involving up to 641 modular robots and experiments on 6 robots have shown that DCIR scales well with the number of modular robots, runs in real time, and reduces traveling distances by at least 14% and communication costs by about half, compared to the distributed control with homogeneous task exchange and the modified surface sliding method. Note to Practitioners—This paper presents a distributed control approach to transform the morphologies. Considering the limited communication bandwidth, the disconnections between modular robots are minimized. The proposed distributed control approach refines tasks locally to transform the morphologies, and it scales well to the number of modular robots. This effort is orthogonal to the existing studies on the structures of the modular system. However, the positioning of the underwater robots in this study was assumed known or given by an underwater motion capture system, and it should be further investigated.
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通过异构分解和分布式控制实现水下可自重构模块化机器人的形态转换
研究了水下自重构模块化机器人系统的形态变换问题。减少了形态学分解和重连接,以减轻变换失败和水下无线通信的开销,从而产生子图匹配问题。我们提出了一种有效的概率分解方法,通过约束初始形态和目标形态的最大公共子图的搜索深度。计算复杂度从$O(n^{2})$降低到$O(n)$。分解产生了一群异构集群,这些集群是相互连接的不同数量的模块化机器人。这种异质性使得簇在目标形态中指定位置的交换不是立即可行的。随后,我们提出了具有最小原位任务优化(DCIR)的分布式控制。实验证明DCIR可以保证无碰撞和无死锁的形态学转换。通过641个模块化机器人的数值模拟和6个机器人的实验表明,与同构任务交换和改进的表面滑动方法相比,DCIR可以随模块化机器人数量的增加而扩展,实时运行,并且至少减少14%的行驶距离和大约一半的通信成本。从业人员注意事项——本文提出了一种分布式控制方法来转换形态。考虑到有限的通信带宽,模块化机器人之间的断线最小。所提出的分布式控制方法在局部细化任务以实现形态学的转换,并且可以很好地扩展到模块化机器人的数量。这项工作与现有的模块化系统结构研究是正交的。然而,在本研究中,水下机器人的定位是假设已知或由水下运动捕捉系统给出的,这需要进一步研究。
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来源期刊
IEEE Transactions on Automation Science and Engineering
IEEE Transactions on Automation Science and Engineering 工程技术-自动化与控制系统
CiteScore
12.50
自引率
14.30%
发文量
404
审稿时长
3.0 months
期刊介绍: The IEEE Transactions on Automation Science and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. T-ASE welcomes results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, security, service, supply chains, and transportation. T-ASE addresses a research community willing to integrate knowledge across disciplines and industries. For this purpose, each paper includes a Note to Practitioners that summarizes how its results can be applied or how they might be extended to apply in practice.
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