TY - GEN
T1 - Automated Control of Multifunctional Magnetic Spores Using Fluorescence Imaging for Microrobotic Cargo Delivery
AU - Yang, Lidong
AU - Zhang, Yabin
AU - Vong, Chi Ian
AU - Zhang, Li
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10
Y1 - 2018/10
N2 - Microrobotic cargo delivery possesses promising perspective for precision medicine, and has attracted much attention recently. However, its automation remains challenging, especially with complex environmental conditions, such as obstacles and obstructed optical feedback. In this paper, we propose an automated control approach for a new microrobotic cargo carrier, i. e. the multifunctional magnetic spore (Mag-Spore). By surface functionalization of the spore with Fe3O4 nanoparticles and carbon quantum dots, it can be remotely actuated and tracked by an electromagnetic coil system and the fluorescence microscopy, respectively. Our strategy utilizes fluorescence imaging for vision feedback, which enhances the recognition and tracking of Mag-Spores and cells. Then, information of the cells and Mag-Spores for planning and control is identified via image processing, and an optimal path planner with obstacle avoidance capability is designed based on the Particle Swarm Optimization (PSO)algorithm. To make the Mag-Spore follow the planed path accurately, an observer-based trajectory tracking controller is synthesized. Simulations and experiments are conducted to demonstrate the effectiveness of the proposed control approach.
AB - Microrobotic cargo delivery possesses promising perspective for precision medicine, and has attracted much attention recently. However, its automation remains challenging, especially with complex environmental conditions, such as obstacles and obstructed optical feedback. In this paper, we propose an automated control approach for a new microrobotic cargo carrier, i. e. the multifunctional magnetic spore (Mag-Spore). By surface functionalization of the spore with Fe3O4 nanoparticles and carbon quantum dots, it can be remotely actuated and tracked by an electromagnetic coil system and the fluorescence microscopy, respectively. Our strategy utilizes fluorescence imaging for vision feedback, which enhances the recognition and tracking of Mag-Spores and cells. Then, information of the cells and Mag-Spores for planning and control is identified via image processing, and an optimal path planner with obstacle avoidance capability is designed based on the Particle Swarm Optimization (PSO)algorithm. To make the Mag-Spore follow the planed path accurately, an observer-based trajectory tracking controller is synthesized. Simulations and experiments are conducted to demonstrate the effectiveness of the proposed control approach.
UR - http://www.scopus.com/inward/record.url?scp=85063006745&partnerID=8YFLogxK
U2 - 10.1109/IROS.2018.8593790
DO - 10.1109/IROS.2018.8593790
M3 - Conference article published in proceeding or book
AN - SCOPUS:85063006745
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 6180
EP - 6185
BT - 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018
Y2 - 1 October 2018 through 5 October 2018
ER -