Micro/nanorobots have significant potential applications in biomedicine.However,their small size and the need for intricate control make long-distance navigation of microswarms composed of such robots challenging in c...Micro/nanorobots have significant potential applications in biomedicine.However,their small size and the need for intricate control make long-distance navigation of microswarms composed of such robots challenging in complex environments.To address this problem,we have developed a permanent-magnet-actuated microswarm navigation system to achieve precise control of micro/nanorobots in complex fluid environments.The controlled microswarm is composed of monodisperse Fe_(3)O_(4)@PVP nanoclusters synthesized using the polyol method.These nanoclusters can self-assemble into highly controllable microswarm structures under a rotating magnetic field and are then guided by the robotic system for precise navigation.The system uses visual positioning and motion control to enable real-time dynamic navigation.In experiments,it successfully performed autonomous navigation over a 55 mm distance in a transparent channel,with flow rates ranging from 0 to 10 mm/s.It completed the task in 132 s at an average speed of over 0.45 mm/s,with an average trajectory tracking error of only 0.28 mm.These results demonstrate excellent path accuracy and stability under various flow rate conditions,validating the system’s adaptability and efficiency in fluid environments and highlighting its potential for biomedical applications.This study offers a robust and versatile platform for expanding micro/nanorobot applications in biomedicine.展开更多
Actively controllable microswarms have been a rapidly developing research field with appealing characteristics.Autonomous collision-free navigation of microswarms in confined environments is suitable for various appli...Actively controllable microswarms have been a rapidly developing research field with appealing characteristics.Autonomous collision-free navigation of microswarms in confined environments is suitable for various applications,including targeted therapy and delivery.However,several challenges remain unaddressed.First,microswarms possess varying dimensions,and a path planning method suitable to swarms with different dimensions is essential to avoid obstacles.Second,studies on the environment-adaptive navigation of reconfigurable microswarms are limited.Therefore,the planning of the pattern distribution of microswarms based on the local working environment should be examined.This study proposes a deep learning(DL)-based environment-adaptive navigation scheme for swarms.The controller provides reference moving directions for swarms of different sizes in static and dynamic scenarios.Moreover,a pattern-distribution planner was designed to navigate transformable swarms in unstructured environments.To validate the proposed scheme,we applied Fe3O4 nanoparticles swarms as a case study.The proposed scheme enables motion and pattern planning for microrobots of multiple sizes and reconfigurability in various working environments,which could foster a general navigation system for reconfigurable microswarms of different sizes.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.52073222)the Fundamental Research Funds for the Central Universities(Grant No.WUT:104972024JYS0027).
文摘Micro/nanorobots have significant potential applications in biomedicine.However,their small size and the need for intricate control make long-distance navigation of microswarms composed of such robots challenging in complex environments.To address this problem,we have developed a permanent-magnet-actuated microswarm navigation system to achieve precise control of micro/nanorobots in complex fluid environments.The controlled microswarm is composed of monodisperse Fe_(3)O_(4)@PVP nanoclusters synthesized using the polyol method.These nanoclusters can self-assemble into highly controllable microswarm structures under a rotating magnetic field and are then guided by the robotic system for precise navigation.The system uses visual positioning and motion control to enable real-time dynamic navigation.In experiments,it successfully performed autonomous navigation over a 55 mm distance in a transparent channel,with flow rates ranging from 0 to 10 mm/s.It completed the task in 132 s at an average speed of over 0.45 mm/s,with an average trajectory tracking error of only 0.28 mm.These results demonstrate excellent path accuracy and stability under various flow rate conditions,validating the system’s adaptability and efficiency in fluid environments and highlighting its potential for biomedical applications.This study offers a robust and versatile platform for expanding micro/nanorobot applications in biomedicine.
基金funding support from the National Key R&D Program of China(2023YFB4705600)the Hong Kong Research Grants Council(RGC)with Research Impact Fund(R4015-21)+4 种基金the Research Fellow Scheme(RFS2122-4S03)the Strategic Topics Grant(STG1/E-401/23-N,GRF14300621,GRF14301122,GRF14205823,GRF15206223,and GRF25200424)the Guangdong Basic and Applied Basic Research Foundation Project(2023A1515110709)the Research Institute for Advanced Manufacturing(RIAM)of the Hong Kong Polytechnic University(1-CD9F and 1-CDK3)the Startup Fund Project(1-BE9L)of the Hong Kong Polytechnic University。
文摘Actively controllable microswarms have been a rapidly developing research field with appealing characteristics.Autonomous collision-free navigation of microswarms in confined environments is suitable for various applications,including targeted therapy and delivery.However,several challenges remain unaddressed.First,microswarms possess varying dimensions,and a path planning method suitable to swarms with different dimensions is essential to avoid obstacles.Second,studies on the environment-adaptive navigation of reconfigurable microswarms are limited.Therefore,the planning of the pattern distribution of microswarms based on the local working environment should be examined.This study proposes a deep learning(DL)-based environment-adaptive navigation scheme for swarms.The controller provides reference moving directions for swarms of different sizes in static and dynamic scenarios.Moreover,a pattern-distribution planner was designed to navigate transformable swarms in unstructured environments.To validate the proposed scheme,we applied Fe3O4 nanoparticles swarms as a case study.The proposed scheme enables motion and pattern planning for microrobots of multiple sizes and reconfigurability in various working environments,which could foster a general navigation system for reconfigurable microswarms of different sizes.
