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Autonomous environment-adaptive microrobot swarm navigation enabled by deep learning-based real-time distribution planning

Nature Machine Intelligence volume 4, pages 480–493 (2022)Cite this article

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Abstract

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.

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Fig. 1: Autonomy levels of environment-adaptive microrobot swarm navigation.
Fig. 2: The real-time autonomous swarm distribution planning method based on DL.
Fig. 3: The reconfigurable magnetic nanoparticle swarm and its adaptive navigation for task execution.
Fig. 4: Experimental demonstrations of navigation autonomy levels from 0 to 2.
Fig. 5: Task execution using autonomy level 3.
Fig. 6: Experimental demonstrations of autonomy level 4.

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Data availability

The dataset49 (~20 GB) used for training the DNNs is available in figshare (https://doi.org/10.6084/m9.figshare.19149779.v1).

Code availability

All the control and planning algorithms used in this study are available within the Article. Original codes for training the DNNs, sample codes for executing the swarm distribution planning in different environmental morphologies and sample codes50 for executing the trajectory planning in a channel environment are available on GitHub (https://github.com/lidongYang22/Autonomous-microrobot-swarm-navigation) and Zenodo (https://doi.org/10.5281/zenodo.6032452). The integrated software used for experimental validation is available from the corresponding author on reasonable request.

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Acknowledgements

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.).

Author information

Author notes

  1. These authors contributed equally: Lidong Yang, Jialin Jiang.

Authors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong, China

    Lidong Yang, Jialin Jiang, Qinglong Wang & Li Zhang

  2. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China

    Xiaojie Gao & Qi Dou

  3. T-Stone Robotics Institute, The Chinese University of Hong Kong, Hong Kong, China

    Qi Dou & Li Zhang

  4. Multi-Scale Medical Robotics Center, Hong Kong Science Park, Hong Kong, China

    Qi Dou & Li Zhang

  5. Department of Surgery, The Chinese University of Hong Kong, Hong Kong, China

    Li Zhang

  6. Chow Yuk Ho Technology Centre for Innovative Medicine, The Chinese University of Hong Kong, Hong Kong, China

    Li Zhang

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Contributions

L.Y. conceived the study. L.Y. and J.J. designed and implemented the system hardware and software, and performed the experiments. J.J., X.G. and Q.D. designed and coded the DL algorithms. Q.W. conducted the X-ray fluoroscopy experiment. L.Y. and J.J. wrote the manuscript with contributions from all authors. All authors contributed to the scientific discussion. L.Z. supervised the project.

Corresponding author

Correspondence to Li Zhang.

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The authors declare no competing interests.

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Nature Machine Intelligence thanks Eric Diller and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Notes 1–10, Figs. 1–18, Table 1, Video legends 1–12 and refs. 1–3.

Reporting Summary

Supplementary Video 1

Robustness validation of the DNN-based swarm distribution planning method for different environmental morphologies, and comparison of computation time between this method and the traversal-based optimization method.

Supplementary Video 2

Experimental demonstration of the three swarm configurations and the transformations among them.

Supplementary Video 3

Experimental demonstration of the translational motion and rotational motion of the RS.

Supplementary Video 4

Experimental comparisons between manual navigation (autonomy level 0) and automated control (autonomy level 1).

Supplementary Video 5

Experimental demonstration of autonomy level 2, where two sets of experiments with different swarm sizes were conducted to assess the intelligence of the DNN-based swarm distribution planning method.

Supplementary Video 6

Experimental demonstration of autonomy level 3, where a swarm of magnetic nanorobots accomplished the delivery task to a targeted region in a channel environment.

Supplementary Video 7

Experimental demonstration of autonomy level 3, where the reconfigurable magnetic nanorobot swarm navigated in highly curved space with a 150° sharp turn and in a curved narrow channel environment.

Supplementary Video 8

Experimental demonstration of autonomy level 3, where the reconfigurable magnetic nanorobot swarm executed the cooperative micromanipulation task in confined space.

Supplementary Video 9

Experimental demonstration of the fully autonomous environment exploration using the magnetic nanorobot swarm in an unknown channel environment (autonomy level 4).

Supplementary Video 10

Experimental demonstration of the fully autonomous targeted delivery to a region with dynamic obstacles (autonomy level 4).

Supplementary Video 11

Transfer of the autonomy framework to the elliptical vortex-like magnetic nanoparticle swarm.

Supplementary Video 12

Method validation under ultrasound imaging and X-ray fluoroscopy.

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Yang, L., Jiang, J., Gao, X. et al. Autonomous environment-adaptive microrobot swarm navigation enabled by deep learning-based real-time distribution planning. Nat Mach Intell 4, 480–493 (2022). https://doi.org/10.1038/s42256-022-00482-8

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