磁驱动微型机器人的自动群运动控制

IF 3.7 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Autonomous Robots Pub Date : 2023-02-25 DOI:10.1007/s10514-023-10084-x
Pouria Razzaghi, Ehab Al Khatib, Yildirim Hurmuzlu
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引用次数: 1

摘要

小型机器人可以进入大型机器人无法进入的空间。这种途径在药物输送、环境检测和小样本收集等应用中至关重要。然而,有一些任务是不可能只用一个机器人来完成的,包括小规模的装配和制造,微纳米物体的操作,以及基于机器人的小规模材料结构。在本文中,我们将重点关注可以使用一组小型机器人(如模式形成)完成的任务。由于它们的尺寸限制,这些机器人通常是外部驱动的。然而,人们面临着使用单一全局输入来控制一组机器人的挑战。在这项研究中,我们提出了一种控制算法来定位一个群体的个体成员在预定义的位置。在我们之前的工作中,我们提出了一个小型磁驱动微型机器人。电磁线圈系统施加外力,引导微型机器人进行各种运动模式,如旋转行走和翻滚。在本文中,我们提出了这些微型机器人的两种新设计。在第一个设计中,磁铁被放置在身体的中心,以减少微型机器人之间的磁力。在第二种设计中,微型机器人的长度相同,多出两条腿作为支点,并且在设计中改变支点间距,以利用支点行走模式下的变速优势,而在翻滚模式下保持速度不变。本文提出了n个不同长度的百万机器人的位置控制算法,使其从给定的初始位置移动到最终期望位置。这种方法的基础是选择一个完全可控的领导者。然后,其他微机器人的运动通过跟随领导者并确定其适当的支点分离来调节,以实现预期的群体运动。仿真和硬件实验验证了这些结果。
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Automated group motion control of magnetically actuated millirobots

Small-size robots offer access to spaces that are inaccessible to larger ones. This type of access is crucial in applications such as drug delivery, environmental detection, and collection of small samples. However, there are some tasks that are not possible to perform using only one robot including assembly and manufacturing at small scales, manipulation of micro- and nano- objects, and robot-based structuring of small-scale materials. In this article, we focus on tasks that can be achieved using a group of small-scale robots like pattern formation. These robots are typically externally actuated due to their size limitation. Yet, one faces the challenge of controlling a group of robots using a single global input. In this study, we propose a control algorithm to position individual members of a group in predefined positions. In our previous work, we presented a small-scaled magnetically actuated millirobot. An electromagnetic coil system applied external force and steered the millirobots in various modes of motion such as pivot walking and tumbling. In this paper, we propose two new designs of these millirobots. In the first design, the magnets are placed at the center of body to reduce the magnetic attraction force between the millirobots. In the second design, the millirobots are of identical length with two extra legs acting as the pivot points and varying pivot separation in design to take advantage of variable speed in pivot walking mode while keeping the speed constant in tumbling mode. This paper presents an algorithm for positional control of n millirobots with different lengths to move them from given initial positions to final desired ones. This method is based on choosing a leader that is fully controllable. Then, the motions of other millirobots are regulated by following the leader and determining their appropriate pivot separations in order to implement the intended group motion. Simulations and hardware experiments validate these results.

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来源期刊
Autonomous Robots
Autonomous Robots 工程技术-机器人学
CiteScore
7.90
自引率
5.70%
发文量
46
审稿时长
3 months
期刊介绍: Autonomous Robots reports on the theory and applications of robotic systems capable of some degree of self-sufficiency. It features papers that include performance data on actual robots in the real world. Coverage includes: control of autonomous robots · real-time vision · autonomous wheeled and tracked vehicles · legged vehicles · computational architectures for autonomous systems · distributed architectures for learning, control and adaptation · studies of autonomous robot systems · sensor fusion · theory of autonomous systems · terrain mapping and recognition · self-calibration and self-repair for robots · self-reproducing intelligent structures · genetic algorithms as models for robot development. The focus is on the ability to move and be self-sufficient, not on whether the system is an imitation of biology. Of course, biological models for robotic systems are of major interest to the journal since living systems are prototypes for autonomous behavior.
期刊最新文献
Optimal policies for autonomous navigation in strong currents using fast marching trees A concurrent learning approach to monocular vision range regulation of leader/follower systems Correction: Planning under uncertainty for safe robot exploration using gaussian process prediction Dynamic event-triggered integrated task and motion planning for process-aware source seeking Continuous planning for inertial-aided systems
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