A Control Strategy of Multiple Microrobots Using a Hybrid Electromagnetic System
Source abstract
Abstract
Abstract Magnetic microrobots are controlled to exhibit a wide range of motions, allowing them to navigate complex environments and perform multifunctional tasks with high precision. This work presents a novel hybrid electromagnetic actuation system by integrating two distinct conventional configurations, such as a paired‐coils electromagnetic disc (EMD) system and a distributed electromagnetic array coil (EAC) system. In order to ensure the effective functioning of the microrobot, its motion dynamics are thoroughly analyzed to identify the critical kinetic parameters. For demonstration purposes, first, a mixing task is performed by employing a single microrobot actuated with simultaneous motions. The mixing efficiency is observed to reach 83% within 30 s, in contrast to the efficiency of control of 45%. Second, a structural reconfiguration function is demonstrated by employing an independent control of two U‐shaped microrobots to form a new I‐shaped microrobot. Last, differentiated motion control of multiple magnetic pads is demonstrated, resulting in various 2D static formations in the shapes of numbers and alphabets. The presented results hold great promise for advancing the field of microrobotics by offering a novel solution for versatile microrobot motion controls.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Micro and Nano Robotics · Modular Robots and Swarm Intelligence · Molecular Communication and Nanonetworks
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mechanical Engineering National Cheng Kung University Tainan 701 Taiwan
Funding
National Science and Technology Council
References (76)
Show source-provided references
1. 10.1186/s40638-014-0018-z10.1186/s40638-014-0018-z ↗
2. 10.1016/j.mechatronics.2013.07.00810.1016/j.mechatronics.2013.07.008 ↗
3. Hu M. · Pharmaceutics · 2020 · 12
4. 10.1073/pnas.171603411410.1073/pnas.1716034114 ↗
5. Pu R. · Front. Bioeng. Biotechnol. · 2024 · 12
6. 10.1002/adfm.20220988310.1002/adfm.202209883 ↗
7. 10.1109/JMEMS.2010.204189710.1109/JMEMS.2010.2041897 ↗
8. 10.3390/mi1303048110.3390/mi13030481 ↗
9. 10.1126/sciadv.adh526010.1126/sciadv.adh5260 ↗
10. 10.1016/j.matdes.2023.11173510.1016/j.matdes.2023.111735 ↗
11. 10.1109/OJNANO.2021.305049610.1109/OJNANO.2021.3050496 ↗
12. 10.1002/aisy.20220020810.1002/aisy.202200208 ↗
13. 10.1126/sciadv.adk725110.1126/sciadv.adk7251 ↗
14. 10.1038/s41467-024-46407-410.1038/s41467-024-46407-4 ↗
15. 10.1002/aisy.20230047110.1002/aisy.202300471 ↗
16. 10.1002/advs.20230838210.1002/advs.202308382 ↗
17. 10.3390/mi1407143910.3390/mi14071439 ↗
18. 10.3390/mi1506079810.3390/mi15060798 ↗
19. 10.1002/admt.20240029210.1002/admt.202400292 ↗
20. 10.3390/mi809026010.3390/mi8090260 ↗
21. 10.1002/admt.20210015010.1002/admt.202100150 ↗
22. 10.1016/j.jmmm.2022.16914110.1016/j.jmmm.2022.169141 ↗
23. 10.1016/j.snb.2016.12.11310.1016/j.snb.2016.12.113 ↗
24. 10.1039/c3lc50407g10.1039/c3lc50407g ↗
25. 10.1038/s41378-023-00611-210.1038/s41378-023-00611-2 ↗
26. 10.1016/j.matchemphys.2023.12806810.1016/j.matchemphys.2023.128068 ↗
27. 10.1016/bs.aams.2015.10.00110.1016/bs.aams.2015.10.001 ↗
28. 10.3390/mi1305073510.3390/mi13050735 ↗
29. Panigrahi B. · iScience · 2021 · 24
30. 10.1073/pnas.230451912010.1073/pnas.2304519120 ↗
31. 10.1007/s00542-017-3428-310.1007/s00542-017-3428-3 ↗
32. 10.1016/j.cep.2015.10.02310.1016/j.cep.2015.10.023 ↗
33. 10.1002/admt.20230224310.1002/admt.202302243 ↗
34. 10.1016/j.sna.2018.10.04510.1016/j.sna.2018.10.045 ↗
35. 10.1038/s41378-018-0010-910.1038/s41378-018-0010-9 ↗
36. 10.1016/j.sna.2020.11218410.1016/j.sna.2020.112184 ↗
37. T. W. R.Fountain P. V.Kailat J. J.Abbott IEEE Int Conf Robot Autom3‐7 May2010.
38. 10.3390/mi609134610.3390/mi6091346 ↗
39. 10.1016/j.sna.2011.08.02010.1016/j.sna.2011.08.020 ↗
40. 10.1016/j.mtbio.2020.10008510.1016/j.mtbio.2020.100085 ↗
41. Shao Y. · Front. Neurosci. · 2021 · 15
42. 10.1002/advs.20210386310.1002/advs.202103863 ↗
43. 10.1088/0964-1726/18/5/05500710.1088/0964-1726/18/5/055007 ↗
44. 10.1016/j.sna.2009.11.01110.1016/j.sna.2009.11.011 ↗
45. 10.1038/srep2871710.1038/srep28717 ↗
46. 10.1016/j.sna.2010.04.03710.1016/j.sna.2010.04.037 ↗
47. Zhou J. · Front. Rob. AI · 2023 · 10
48. Sajjamark K. · Int. J. Magn. Part. Imaging · 2020 · 6
49. 10.1016/j.jmmm.2022.17015910.1016/j.jmmm.2022.170159 ↗
50. 10.3390/mi1312214410.3390/mi13122144 ↗
51. 10.1002/aisy.20230048310.1002/aisy.202300483 ↗
52. 10.1073/pnas.192009911710.1073/pnas.1920099117 ↗
53. 10.1017/jfm.2015.44210.1017/jfm.2015.442 ↗
54. 10.1103/PhysRevFluids.2.01310310.1103/PhysRevFluids.2.013103 ↗
55. 10.1002/admt.20220107310.1002/admt.202201073 ↗
56. Dong X. · Int. J. Rob. Res. · 2019 · 39
57. Medina‐Sánchez M. · Adv. Funct. Mater. · 2018 · 28
58. 10.1126/scirobotics.aav800610.1126/scirobotics.aav8006 ↗
59. 10.1038/s42256-022-00482-810.1038/s42256-022-00482-8 ↗
60. 10.1021/acsnano.4c0211510.1021/acsnano.4c02115 ↗
61. 10.1038/s41467-019-13576-610.1038/s41467-019-13576-6 ↗
62. 10.3390/biomimetics801010910.3390/biomimetics8010109 ↗
63. 10.1016/j.isci.2023.10672710.1016/j.isci.2023.106727 ↗
64. S.Martel M.Mohammadi presented at 2010 IEEE Internationa2010 l Conference on Robotics and Automation 3‐7 May2010.
65. 10.1021/acsnano.2c0293810.1021/acsnano.2c02938 ↗
66. 10.3390/mi1311198710.3390/mi13111987 ↗
67. 10.1007/s43154-022-00085-610.1007/s43154-022-00085-6 ↗
68. 10.1002/aisy.20240040310.1002/aisy.202400403 ↗
69. 10.1002/advs.20190147810.1002/advs.201901478 ↗
70. 10.3934/mbe.202146110.3934/mbe.2021461 ↗
71. 10.1039/D2LC00758D10.1039/D2LC00758D ↗
72. 10.1016/j.prostr.2022.01.09910.1016/j.prostr.2022.01.099 ↗
73. Chen C.‐Y. · Biomicrofluidics · 2015 · 9
74. 10.1016/j.cej.2014.07.12010.1016/j.cej.2014.07.120 ↗
75. 10.1115/1.294836410.1115/1.2948364 ↗
76. 10.1007/s00348-014-1765-x10.1007/s00348-014-1765-x ↗