Real-time motion planning of multiple nanowires in fluid suspension under electric-field actuation
Tóm tắt
The ability to precisely and efficiently manipulate nano- and micro-scale objects is a key step in enabling applications in areas such as nano-medicine and nano/micro-device assembly. In this paper, we propose and demonstrate real-time motion-planning algorithms for effectively steering multiple nanowires simultaneously in fluid suspension using electric fields. The proposed motion planners
$$\mathtt{{SRRT}}^*$$
and learning-
$$\mathtt{{SRRT}}^*$$
(
$$\mathtt{{LSRRT}}^*$$
) are built on and extended by sparse-structure, Rapidly-exploring Random Tree (RRT)-based motion-planning algorithms. The algorithms also use heuristics to effectively guide the search process to quickly generate initial solutions. In order to reduce the online computational cost, the
$$\mathtt{{LSRRT}}^*$$
algorithm shifts the intensive computation of optimal additive cost metric to offline training using a supervised learning method. Compared with the previously developed network-flow and RRT-based algorithms, the
$$\mathtt{{SRRT}}^*$$
and
$$\mathtt{{LSRRT}}^*$$
guarantee the asymptotically near-optimal trajectories of multiple nanowires. Moreover, the algorithms do not restrict in theory the maximum number of nanowires that can be simultaneously controlled. We present simulation and experimental results to demonstrate the minimum-time performance of the motion-planning and control design. The results demonstrate that the proposed algorithms significantly reduce the computational cost and increase the efficiency of manipulating multiple nanowires simultaneously when compared with the
$${\mathtt{RRT}}^*$$
and other
$${\mathtt{RRT}}^*$$
enhanced algorithms.
Tài liệu tham khảo
Akin, C., Feldman, L.C., Durand, C., Hus, S.M., Li, A.-P., Hui, H.Y., Filler, M.A., Yi, J., Shan, J.W.: High-throughput electrical measurement and microfluidic sorting of semiconductor nanowires. Lab Chip 16(11), 2126–2134 (2016)
Barraquand, J., Latombe, J.C.: Nonholonomic multibody mobile robots: controllability and motion planning in the presence of obstacles. Algorithmica 10(2–4), 121–155 (1993)
Bharatheesha, M., Caarls, W., Wolfslag, W.J., Wisse, M.: Distance metric approximation for state-space RRTs using supervised learning. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago, IL, USA, pp. 252–257 (2014)
Cao, Q., Rogers, J.A.: Ultrathin films of single-walled carbon nanotubes for electronics and sensors: a review of fundamental and applied aspects. Adv. Mater. 21(1), 29–53 (2009)
Cohen, B.J., Chitta, S., Likhachev, M.: Search-based planning for manipulation with motion primitives. In: Proceedings of IEEE international conference on robotics automatiom, Anchorage, AK, USA, May pp. 2902–2908 (2010)
Dobson, A., Bekris, K.E.: Sparse roadmap spanners for asymptotically near-optimal motion planning. Int. J. Robot. Res. 33(1), 18–47 (2014)
Fan, D., Yin, Z., Cheong, R., Zhu, F.Q., Cammarata, R.C., Chien, C.L., Levchenko, A.: Subcellularresolution delivery of a cytokine through precisely manipulated nanowires. Nat. Nanotech. 5, 545–551 (2010)
Fan, D.L., Zhu, F.Q., Cammarata, R.C., Chien, C.L.: Electric tweezers. Nano Today 6, 339–354 (2011)
Furtuna, A.A.: Minimum time kinematic trajectories for self-propelled rigid bodies in the unobstructed plane. Ph.D. dissertation, Department of Computer Science, Dartmouth College, Hanover, NH (2011)
Gammell, J.D., Srinivasa, S.S., Barfoot, T.D.: Informed \(\text{RRT}^*\): Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic. In: Proceedings of IEEE/RSJ international conference on intelligent robots and systems. Chicago, IL, pp. 2997–3004 (2014)
Goodfellow, I.: Nips 2016 tutorial: Generative adversarial networks. arXiv preprint arXiv:1701.00160, (2016)
Griffith, E.J., Akella, S., Goldberg, M.K.: Performance characterization of a reconfigurable planar-array digital microfluidic system. IEEE Trans. Comput.Aided Design Integr. Circ. Syst. 25(2), 345–357 (2006)
Hastie, T., Tibshirani, R., Friedman, J.: The elements of statistical learning. Springer, New York (2001)
Hilmer, A.J., Strano, M.S.: Nanobiotechnology: Nanowires have cells in their sights. Nat. Nanotech. 5(7), 481–482 (2010)
Hunter, R.: Foundations of colloid science. Oxford University Press, Oxford (1989)
Ju, T., Liu, S., Yang, J., Sun, D.: Rapidly exploring random tree algorithm-based path planning for robot-aided optical manipulation of biological cells. IEEE Trans. Automat. Sci. Eng. 11(3), 649–657 (2014)
Karaman, S., Frazzoli, E.: Sampling-based algorithms for optimal motion planning. Int. J. Robot. Res. 30(7), 846–894 (2011)
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, (2013)
LaValle, S.: Planning algorithms. Cambridge University Press, New York (2006)
Li, Y., Bekris, K.E.: Learning approximate cost-to-go metrics to improve sampling-based motion planning. In: Proceedings of IEEE international conference on robotics and automation. Shanghai, China (2011)
Li, Y., Littlefield, Z., Bekris, K.E.: Asymptotically optimal sampling-based kinodynamic planning. Int. J. Robot. Res. 35(5), 528–564 (2016)
Littlefield, Z., Bekris, K.E.: Informed asymptotically near-optimal planning for field robots with dynamics. Field and service robotics, pp. 449–463. Springer, Berlin (2018)
Moerland, T.M., Broekens, J., Jonker, C.M.: Learning multimodal transition dynamics for model-based reinforcement learning. arXiv preprint arXiv:1705.00470, (2017)
Probst, R., Cummins, Z., Ropp, C., Eaks, E., Shapiro, B.: Flow control of small objects on chip: manipulating live cells, quantum dots, and nanowires. IEEE Control Syst. Mag. 32(2), 26–53 (2012)
Tedrake, R.: LQR-trees: feedback motion planning on sparse randomized trees. In: Proceedings of Robotics: Science and System, Seattle, USA, (2009)
Wolfslag, W., Bharatheesha, M., Moerland, T., Wisse, M.: RRT-CoLearn: towards kinodynamic planning without numerical trajectory optimization. IEEE Robot. Automat. Lett. 3(3), 1655–1662 (2018)
Yu, K., Lu, X., Yi, J., Shan, J.: Electrophoresis-based motion planning and control of nanowires in suspended fluids. In: Proceedings of IEEE Conference on Automation Science and Engineering. Madison, WI, pp. 831–836 (2013)
Yu, K., Yi, J., Shan, J.: Motion control and manipulation of nanowires under electric-fields in fluid suspension. In: Proceedings of IEEE/ASME international conference on advanced intelligence mechatronics, Besancon, France, pp. 366–371 (2014)
Yu, K., Yi, J., Shan, J.: Motion planning and manipulation of multiple nanowires simultaneously under electric-fields in fluid suspension. In: Proceedings of IEEE conference on automation science and engineering. Gothenburg, Sweden, pp. 489–494 (2015a)
Yu, K., Yi, J., Shan, J.: Motion control, planning and manipulation of nanowires under electric-fields in fluid suspension. IEEE Trans. Automat. Sci. Eng. 12(1), 37–49 (2015b)
Yu, K., Yi, J., Shan, J.: Time-optimal simultaneous motion planning and manipulation of multiple nanowires under electric-fields in fluid suspension. In: Procedings of IEEE conference automation science engineering, Dallas, TX, pp. 954–959 (2016)
Yu, K., Yi, J., Shan, J.W.: Automated characterization and assembly of individual nanowires for device fabrication. Lab Chip 18(10), 1494–1503 (2018a)
Yu, K., Yi, J., Shan, J.: Simultaneous multiple-nanowire motion control, planning, and manipulation under electric fields in fluid suspension. IEEE Trans. Automat. Sci. Eng. 15(1), 80–91 (2018b)
Yuh, P.-H., Yang, C.-L., Chang, Y.-W.: BioRoute: a network-flow-based routing algorithm for the synthesis of digitalmicrofluidic biochips. IEEE Trans. Comput. Aided Design Integr. Circ. Syst. 27(11), 1928–1941 (2008)