TY - GEN
T1 - Large-Workspace and High-Resolution Magnetic Microrobot Navigation Using Global-Local Path Planning and Eye-in-Hand Visual Servoing
AU - Yang, Lidong
AU - Zhang, Li
N1 - Funding Information:
This work was supported by the RGC General Research Fund (GRF) with Project No. 14218516 funded by the Research Grants Council (RGC) of Hong Kong and the ITF project with Project No. MRP/036/18X funded by the HKSAR Innovation and Technology Commission (ITC).
Publisher Copyright:
© 2020 IEEE.
PY - 2020/8
Y1 - 2020/8
N2 - Magnetic microrobots have the capability of navigation in confined and narrow space to perform delivery or micromanipulation tasks. However, due to the fast decay of magnetic fields and the contradiction between resolution and field of view (FOV) of a feedback instrument, the large-workspace and high-resolution (LWHR) navigation remains a challenge in magnetic microrobotics. This paper provides a solution to this challenging problem, in which a self-constructed magnetic manipulation system with mobile electromagnetic coils and eye-in-hand feedback is used to actuate magnetic microrobots in a large workspace. To enable the automated large-workspace microrobot navigation in complex environments, we propose a global-local path planning scheme. In the mode of low-resolution feedback, the whole workspace is captured and a global near-optimal path is planned. On the other hand, in the high-resolution feedback mode, the microrobot and its local environment is precisely tracked and identified, respectively. Then, a real-time local planning algorithm is designed to correct the deficiencies in the rough global path. Parameter tuning of the planning scheme is accomplished via simulations. Closed-loop motion control and field control algorithms are designed and implemented, and experiments demonstrate the automated LWHR navigation of magnetic microrobots in a vascular-like network. Results show that the ratio between the navigation diameter and the microrobot diameter exceeds 200.
AB - Magnetic microrobots have the capability of navigation in confined and narrow space to perform delivery or micromanipulation tasks. However, due to the fast decay of magnetic fields and the contradiction between resolution and field of view (FOV) of a feedback instrument, the large-workspace and high-resolution (LWHR) navigation remains a challenge in magnetic microrobotics. This paper provides a solution to this challenging problem, in which a self-constructed magnetic manipulation system with mobile electromagnetic coils and eye-in-hand feedback is used to actuate magnetic microrobots in a large workspace. To enable the automated large-workspace microrobot navigation in complex environments, we propose a global-local path planning scheme. In the mode of low-resolution feedback, the whole workspace is captured and a global near-optimal path is planned. On the other hand, in the high-resolution feedback mode, the microrobot and its local environment is precisely tracked and identified, respectively. Then, a real-time local planning algorithm is designed to correct the deficiencies in the rough global path. Parameter tuning of the planning scheme is accomplished via simulations. Closed-loop motion control and field control algorithms are designed and implemented, and experiments demonstrate the automated LWHR navigation of magnetic microrobots in a vascular-like network. Results show that the ratio between the navigation diameter and the microrobot diameter exceeds 200.
UR - http://www.scopus.com/inward/record.url?scp=85094101791&partnerID=8YFLogxK
U2 - 10.1109/CASE48305.2020.9216900
DO - 10.1109/CASE48305.2020.9216900
M3 - Conference article published in proceeding or book
AN - SCOPUS:85094101791
T3 - IEEE International Conference on Automation Science and Engineering
SP - 876
EP - 881
BT - 2020 IEEE 16th International Conference on Automation Science and Engineering, CASE 2020
PB - IEEE Computer Society
T2 - 16th IEEE International Conference on Automation Science and Engineering, CASE 2020
Y2 - 20 August 2020 through 21 August 2020
ER -