Safety-Enhanced Navigation Planning for Magnetic Microrobots

IF 6.4 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Automation Science and Engineering Pub Date : 2025-01-08 DOI:10.1109/TASE.2025.3525669
Yueyue Liu;Linfeng Zhang;Xinyu Liu;Qigao Fan
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Abstract

Magnetic microrobots demonstrate significant potential in medical applications by providing innovative solutions for precise treatment through targeted drug delivery, minimally invasive surgery, and vascular cleaning. However, within biological organisms, there are various complex obstacle environments that require a navigation technology prioritizing safety and emphasizing smoothness. This paper proposes a safety-enhanced navigation planning (SENP) algorithm to achieve multiple objectives such as safety, path smoothness, and short distance, enabling collision-free navigation in complex medical environments. Unlike traditional methods that require multiple heuristic cost functions to guide the navigation planning algorithm, our approach leverages the safety-enhancing features of the safe artificial potential field (SAPF) to promote collision-free navigation in complex medical environments. By adopting a goal-biased strategy to guide the generation of random sampling points, the number of iterations is reduced, and the convergence speed of the algorithm is improved. In addition, the traditional artificial potential field planning method often leads to the oscillation of the corridor, our method effectively addresses this issue along with the uncertainty in the quality of the initial path and the lengthy convergence time to the optimal path. Comparative analysis with various algorithms in different environments shows that our proposed method excels in terms of smoothness and path length under the premise of safety, making it suitable for magnetic microrobots in complex environments. Note to Practitioners—The motivation for this work lies in advancing safe, efficient, and rapid navigation strategies for magnetic microrobots in medical applications. While significant progress has been made in the development of magnetic microrobots, navigating through complex environments such as human blood vessels remains a substantial challenge. The ability to perform safe and effective motion planning within narrow and intricate channels is crucial for medical applications. In response to this need, we propose a path planning method specifically designed for magnetically actuated microrobots, based on SENP. Our approach emphasizes achieving a path that is not only short and smooth but also prioritizes safety throughout the navigation process. Compared to traditional sampling-based algorithms, our method effectively overcomes limitations such as initial path quality uncertainty and prolonged convergence to optimal paths. This allows for the rapid generation of a high-quality initial path with a focus on safety while significantly accelerating convergence. Moreover, our approach provides a robust solution for navigating narrow channels, making it highly suitable for challenging medical environments.
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磁性微型机器人的安全增强导航规划
磁性微型机器人通过提供创新的解决方案,通过靶向药物输送、微创手术和血管清洁进行精确治疗,在医疗应用中展示了巨大的潜力。然而,在生物有机体中,存在各种复杂的障碍环境,需要优先考虑安全性和强调平滑性的导航技术。该文提出了一种安全增强导航规划(SENP)算法,以实现安全、路径平滑、短距离等多个目标,实现复杂医疗环境下的无碰撞导航。与传统方法需要多个启发式成本函数来指导导航规划算法不同,我们的方法利用安全人工势场(SAPF)的安全增强特性来促进复杂医疗环境中的无碰撞导航。通过采用目标偏置策略引导随机采样点的生成,减少了迭代次数,提高了算法的收敛速度。此外,传统的人工势场规划方法往往会导致走廊振荡,本文的方法有效地解决了这一问题以及初始路径质量的不确定性和到最优路径收敛时间长的问题。与不同环境下各种算法的对比分析表明,在保证安全的前提下,我们提出的方法在平滑度和路径长度方面都有很好的表现,适合于复杂环境下的磁性微型机器人。从业人员注意:这项工作的动机在于推进磁微型机器人在医疗应用中的安全、高效和快速导航策略。虽然磁性微型机器人的发展取得了重大进展,但在人类血管等复杂环境中导航仍然是一个重大挑战。在狭窄和复杂的通道内进行安全有效的运动规划的能力对医疗应用至关重要。针对这一需求,我们提出了一种基于SENP的磁驱动微型机器人路径规划方法。我们的方法强调在整个航行过程中,不仅要实现短而平稳的路径,而且要优先考虑安全。与传统的基于采样的算法相比,该方法有效地克服了初始路径质量不确定性和收敛时间过长等局限性。这允许快速生成高质量的初始路径,重点是安全,同时显著加速收敛。此外,我们的方法为导航窄通道提供了强大的解决方案,使其非常适合具有挑战性的医疗环境。
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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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