TY - JOUR
T1 - Autonomous environment-adaptive microrobot swarm navigation enabled by deep learning-based real-time distribution planning
AU - Yang, Lidong
AU - Jiang, Jialin
AU - Gao, Xiaojie
AU - Wang, Qinglong
AU - Dou, Qi
AU - Zhang, Li
N1 - Funding Information:
We would like to thank S. Yang and D. Jin for help with the ultrasound experiments, T. Lam and K. Lai for help with the X-ray fluoroscopy experiments and K.-F. Chan for applying for permission for the navigation experiments in human placenta. We also would like to thank Prof. Ben M. Chen for the fruitful discussion.This project has received funding support from the Hong Kong Research Grants Council (E-CUHK401/20)(to L.Z.), the ITF project MRP/036/18X (to L.Z.), the Croucher Foundation grant CAS20403 (to L.Z.), CUHK internal grants (to L.Z.), the Multi-Scale Medical Robotics Center (MRC), InnoHK, at the Hong Kong Science Park (to L.Z.), the SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems (to L.Z.) and the CUHK Shun Hing Institute of Advanced Engineering (MMT-p5-20) (to Q.D.).
Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Nature Limited.
PY - 2022/5
Y1 - 2022/5
N2 - Navigating a large swarm of micro-/nanorobots is critical for potential targeted delivery/therapy applications owing to the limited volume/function of a single microrobot, and microrobot swarms with distribution reconfigurability can adapt to environments during navigation. However, current microrobot swarms lack the intelligent behaviour to autonomously adjust their distribution and motion according to environmental change. Such autonomous navigation is challenging, and requires real-time appropriate decision-making capability of the swarm for unknown and unstructured environments. Here, to tackle this issue, we propose a framework that defines different autonomy levels for environment-adaptive microrobot swarm navigation and designs corresponding system components for each level. To realize high autonomy levels, real-time autonomous distribution planning is a key capability for the swarm, regarding which we show that deep learning is an enabling approach that allows the microrobot swarm to learn optimal distributions in extensive unstructured environmental morphologies. For real-world demonstration, we study the reconfigurable magnetic nanoparticle swarm and experimentally demonstrate autonomous swarm navigation for targeted delivery and cargo transport in environments with channels or obstacles. This work could introduce computational intelligence to micro-/nanorobot swarms, enabling them to autonomously make appropriate decisions during navigation in unstructured environments.
AB - Navigating a large swarm of micro-/nanorobots is critical for potential targeted delivery/therapy applications owing to the limited volume/function of a single microrobot, and microrobot swarms with distribution reconfigurability can adapt to environments during navigation. However, current microrobot swarms lack the intelligent behaviour to autonomously adjust their distribution and motion according to environmental change. Such autonomous navigation is challenging, and requires real-time appropriate decision-making capability of the swarm for unknown and unstructured environments. Here, to tackle this issue, we propose a framework that defines different autonomy levels for environment-adaptive microrobot swarm navigation and designs corresponding system components for each level. To realize high autonomy levels, real-time autonomous distribution planning is a key capability for the swarm, regarding which we show that deep learning is an enabling approach that allows the microrobot swarm to learn optimal distributions in extensive unstructured environmental morphologies. For real-world demonstration, we study the reconfigurable magnetic nanoparticle swarm and experimentally demonstrate autonomous swarm navigation for targeted delivery and cargo transport in environments with channels or obstacles. This work could introduce computational intelligence to micro-/nanorobot swarms, enabling them to autonomously make appropriate decisions during navigation in unstructured environments.
UR - http://www.scopus.com/inward/record.url?scp=85130128014&partnerID=8YFLogxK
U2 - 10.1038/s42256-022-00482-8
DO - 10.1038/s42256-022-00482-8
M3 - Journal article
AN - SCOPUS:85130128014
SN - 2522-5839
VL - 4
SP - 480
EP - 493
JO - Nature Machine Intelligence
JF - Nature Machine Intelligence
IS - 5
ER -