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Micromanipulation Using Reconfigurable Self-Assembled Magnetic Droplets With Needle Guidance
- Source :
- IEEE Transactions on Automation Science and Engineering. 19:759-771
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
-
Abstract
- A dynamic self-assembly is a promising approach for inducing the collective behavior of agents to perform coordinated tasks at small scales. However, efficient pattern formation and navigation in environments with complex conditions remain a challenge. In this article, we propose a strategy for micromanipulation using dynamically self-assembled magnetic droplets with needle guidance. An iron needle was controlled by a three-degree-of-freedom (3-DoF) manipulator and magnetized by precessing magnetic fields. The process of self-assembly was optimized based on real-time vision feedback and a genetic algorithm. Affected by the locally induced field gradient near the needle, reconfigurable assembled magnetic droplets were formed beneath the air-liquid interface with high time efficiency, and the geometric center of the pattern was determined. Following the magnetized needle, assembled patterns were navigated along preplanned paths and exhibited reversible pattern expansion and shrinkage. Moreover, cargo can be trapped and caged by exploiting the induced fluid flow around the assembled droplets. To perform cargo transportation tasks in a multiple-obstacle environment, an optimal path planner with obstacle-avoidance capability was designed based on the particle swarm optimization (PSO) algorithm. Experiments demonstrated effective pattern formation, navigation, cargo trapping, and obstacle-avoidance transportation. The proposed method opens new prospects of using a dynamically self-assembled pattern as an untethered end-effector for micromanipulation.
- Subjects :
- Collective behavior
Computer science
business.industry
Interface (computing)
Process (computing)
Pattern formation
Particle swarm optimization
Magnetic field
Computer Science::Robotics
Control and Systems Engineering
Genetic algorithm
Path (graph theory)
Electrical and Electronic Engineering
Aerospace engineering
business
Subjects
Details
- ISSN :
- 15583783 and 15455955
- Volume :
- 19
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Automation Science and Engineering
- Accession number :
- edsair.doi...........68d38c59c6cabd5553b7eb95a7c40607