Nhóm robot không đồng nhất cho việc mô hình hóa và dự đoán các quá trình môi trường đa quy mô

Andersone, I. (2019). Heterogeneous map merging: State of the art. Robotics, 8(3), 1–29. https://doi.org/10.3390/robotics8030074 Bae, J., Lee, J., & Chung, W. (2019). A heuristic for task allocation and routing of heterogeneous robots while minimizing maximum travel cost. In Proceedings—IEEE international conference on robotics and automation (pp. 4531–4537). https://doi.org/10.1109/ICRA.2019.8794257 Berger, E., Sastuba, M., Vogt, D., Jung, B., & Ben Amor, H. (2014). Estimation of perturbations in robotic behavior using dynamic mode decomposition. Advanced Robotics, 10(1080/01691864), 981292. Brand, M. (2002). Incremental singular value decomposition (SVD) of incomplete data. Tech. rep., Mitsubishi Electric Research Labs, http://www.merl.com Brunton, B. W., Johnson, L. A., Ojemann, J. G., & Kutz, J. N. (2016). Extracting spatial–temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition. Journal of Neuroscience Methods, 258, 1–15. https://doi.org/10.1016/j.jneumeth.2015.10.010 Budišić, M., Mohr, R., & Mezić, I. (2012). Applied Koopmanism. Chaos. https://doi.org/10.1063/1.4772195 Cortés, J., Martínez, S., Karatas, T., Bullo, F., & Member, S. (2004). Coverage control for mobile sensing networks. IEEE Transactions on Robotics and Automation, 20(2), 243–255. https://doi.org/10.1109/TRA.2004.824698 Demo, N., Tezzele, M., & Rozza, G. (2018). PyDMD: Python dynamic mode decomposition. The Journal of Open Source Software, 3(22), 530. https://doi.org/10.21105/joss.00530 Dunbabin, M., & Marques, L. (2012). Robots for environmental monitoring: Significant advancements and applications. IEEE Robotics and Automation Magazine, 19(1), 24–39. https://doi.org/10.1109/MRA.2011.2181683 Erichson, N. B., Brunton, S. L., & Kutz, J. N. (2019). Compressed dynamic mode decomposition for background modeling. Journal of Real-Time Image Processing. https://doi.org/10.1007/s11554-016-0655-2 Everson, R., & Sirovich, L. (1995). Karhunen–Loève procedure for gappy data. Journal of the Optical Society of America, 12(8), 1657. https://doi.org/10.1364/JOSAA.12.001657 Folkestad, C., Pastor, D., Mezic, I., Mohr, R., Fonoberova, M., & Burdick, J. (2020). Extended dynamic mode decomposition with learned Koopman eigenfunctions for prediction and control. In Proceedings of the American control conference. https://doi.org/10.23919/ACC45564.2020.9147729 Gerkey, B. P., & Matarić, M. J. (2004). A formal analysis and taxonomy of task allocation in multi-robot systems. International Journal of Robotics Research, 23(9), 939–954. https://doi.org/10.1177/0278364904045564 Jones, E. G., Browning, B., Dias, M. B., Argall, B., Veloso, M., & Stentz, A. (2006). Dynamically formed heterogeneous robot teams performing tightly-coordinated tasks. Proceedings - IEEE International Conference on Robotics and Automation, 2006(May), 570–575. https://doi.org/10.1109/ROBOT.2006.1641771 Joshi, S., & Boyd, S. (2009). Sensor selection via convex optimization. IEEE Transactions on Signal Processing. https://doi.org/10.1109/TSP.2008.2007095 Jovanović, M. R., Schmid, P. J., & Nichols, J. W. (2014). Sparsity-promoting dynamic mode decomposition. Physics of Fluids. https://doi.org/10.1063/1.4863670 Julian, B. J., Angermann, M., Schwager, M., & Rus, D. (2012). Distributed robotic sensor networks: An information-theoretic approach. International Journal of Robotics Research, 31(10), 1134–1154. https://doi.org/10.1177/0278364912452675 Khamis, A., Hussein, A., & Elmogy, A. (2015). Multi-robot task allocation: A review of the state-of-the-art. In Cooperative robots and sensor networks 2015 (pp. 31–51). Springer. Korsah, G. A., Stentz, A., & Dias, M. B. (2013). A comprehensive taxonomy for multi-robot task allocation. International Journal of Robotics Research, 32(12), 1495–1512. https://doi.org/10.1177/0278364913496484 Krause, A., & Guestrin, C. (2007). Nonmyopic active learning of Gaussian processes: An exploration–exploitation approach. ACM International Conference Proceeding Series, 227, 449–456. https://doi.org/10.1145/1273496.1273553 Krause, A., Singh, A., & Guestrin, C. (2008). Near-optimal sensor placements in Gaussian processes: Theory, efficient algorithms and empirical studies. Journal of Machine Learning Research, 9, 235–284. https://doi.org/10.1145/1390681.1390689 Liang, C. D., Wang, L., Yao, X. Y., Liu, Z. W., & Ge, M. F. (2019). Multi-target tracking of networked heterogeneous collaborative robots in task space. Nonlinear Dynamics, 97(2), 1159–1173. https://doi.org/10.1007/s11071-019-05038-x Ma, L., Zhu, J., Zhu, L., Du, S., & Cui, J. (2016). Merging grid maps of different resolutions by scaling registration. Robotica. https://doi.org/10.1017/S0263574715000168 Maini, P., Gupta, G., Tokekar, P., & Sujit, P. (2018). Visibility-based monitoring of a path using a heterogeneous robot team. In IEEE international conference on intelligent robots and systems (pp. 3765–3770). https://doi.org/10.1109/IROS.2018.8593960 Manderson, T., Manjanna, S., & Dudek, G. (2019). Heterogeneous robot teams for informative sampling. Workshop on Informative Path Planning and Adaptive Sampling at Robotics Science and Systemshttp://arxiv.org/abs/1906.07208 Manjanna, S., Li, A. Q., Smith, R. N., Rekleitis, I., & Dudek, G. (2018). Heterogeneous multi-robot system for exploration and strategic water sampling. Proceedings—IEEE international conference on robotics and automation (pp. 4873–4880). https://doi.org/10.1109/ICRA.2018.8460759 Manohar, K., Brunton, B. W., Kutz, J. N., & Brunton, S. L. (2018). Data-driven sparse sensor placement for reconstruction: Demonstrating the benefits of exploiting known patterns. IEEE Control Systems, 38(3), 63–86. https://doi.org/10.1109/MCS.2018.2810460 Manohar, K., Kaiser, E., Brunton, S. L., & Kutz, J. N. (2019). Optimized sampling for multiscale dynamics. Multiscale Modeling & Simulation, 17(1), 117–136. https://doi.org/10.1137/17m1162366 Matsumoto, D., & Indinger, T. (2017). On-the-fly algorithm for dynamic mode decomposition using incremental singular value decomposition and total least squares. http://arxiv.org/abs/1703.11004 Mezić, I. (2005). Spectral properties of dynamical systems, model reduction and decompositions. Nonlinear Dynamics, 66, 309–325. https://doi.org/10.1007/s11071-005-2824-x Nashashibi, F., Devy, M., & Fillatreau, P. (1992). Indoor scene terrain modeling using multiple range images for autonomous mobile robots. In Proceedings—IEEE international conference on robotics and automation (pp. 40–46). Notomista, G., Mayya, S., Hutchinson, S., & Egerstedt, M. (2019). An optimal task allocation strategy for heterogeneous multi-robot systems. In 2019 18th European control conference (ECC 2019) (pp. 2071–2076). https://doi.org/10.23919/ECC.2019.8795895 Park, J., Sinclair, A. J., Sherrill, R. E., Doucette, E. A., & Curtis, J. W. (2016). Map merging of rotated, corrupted, and different scale maps using rectangular features. In Proceedings of the IEEE/ION position, location and navigation symposium, PLANS 2016. https://doi.org/10.1109/PLANS.2016.7479743 Parker, L. E. (2003). The effect of heterogeneity in teams of 100+ mobile robots. Tech. rep. Prorok, A., Hsieh, M. A, & Kumar, V. (2015). Fast redistribution of a swarm of heterogeneous robots. In EAI international conference on bio-inspired information and communications technologies (BICT). https://doi.org/10.4108/eai.3-12-2015.2262349 Prorok, A., Hsieh, M. A., & Kumar, V. (2016). Formalizing the impact of diversity on performance in a heterogeneous swarm of robots. In Proceedings—IEEE international conference on robotics and automation (vol. 2016-June, pp. 5364–5371). IEEE. https://doi.org/10.1109/ICRA.2016.7487748 Rasmussen, C. E. (2004). Gaussian Processes in machine learning. In Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) 3176 (advanced lectures on machine learning) (pp. 63–71). https://doi.org/10.1007/978-3-540-28650-9_4 Rossi, C, Aldama, L, & Barrientos, A. (2009). Simultaneous task subdivision and allocation for teams of heterogeneous robots. In Proceedings—IEEE international conference on robotics and automation (pp. 946–951). https://doi.org/10.1109/ROBOT.2009.5152299 Rowley, C. W., Mezi, I., Bagheri, S., Schlatter, P., & Henningson, D. S. (2009). Spectral analysis of nonlinear flows. Journal of Fluid Mechanics, 641, 115–127. https://doi.org/10.1017/S0022112009992059 Sadeghi, A., & Smith, S. L. (2019). Coverage control for multiple event types with heterogeneous robots. In Proceedings—IEEE international conference on robotics and automation 2019-May (pp. 3377–3383). https://doi.org/10.1109/ICRA.2019.8793639 Salam, T., & Hsieh, M. A. (2019). Adaptive sampling and reduced-order modeling of dynamic processes by robot teams. IEEE Robotics and Automation Letters, 4(2), 477–484. https://doi.org/10.1109/LRA.2019.2891475 Santos, M., Diaz-Mercado, Y., & Egerstedt, M. (2018). Coverage control for multirobot teams with heterogeneous sensing capabilities. IEEE Robotics and Automation Letters, 3(2), 919–925. https://doi.org/10.1109/LRA.2018.2792698 Santos, M., & Egerstedt, M. (2018). Coverage control for multi-robot teams with heterogeneous sensing capabilities using limited communications. In IEEE international conference on intelligent robots and systems (pp. 5313–5319). https://doi.org/10.1109/IROS.2018.8594056 Schmid, P. J. (2010). Dynamic mode decomposition of numerical and experimental data. Journal of Fluid Mechanics, 656, 5–28. https://doi.org/10.1017/S0022112010001217 Shahbandi, S. G., & Magnusson, M. (2019). 2D map alignment with region decomposition. Autonomous Robots. https://doi.org/10.1007/s10514-018-9785-7 Singh, A., Krause, A., Guestrin, C., & Kaiser, W. J. (2009). Efficient informative sensing using multiple robots. Journal of Artificial Intelligence Research, 34, 707–755. https://doi.org/10.1613/jair.2674 Singh, A., Ramos, F., Durrant Whyte, H., Kaiser, W. J., Whyte, H. D., & Kaiser, W. J. (2010). Modeling and decision making in spatio-temporal processes for environmental surveillance Amarjeet. In Proceedings—IEEE international conference on robotics and automation (pp. 5490–5497). https://doi.org/10.1109/ROBOT.2010.5509934 Strogatz, S. (2001). Nonlinear dynamics and chaos: With applications to physics, biology, chemistry, and engineering (2nd ed., vol. 32) (studies in nonlinearity). Westview Press. https://doi.org/10.5860/choice.32-0994 Taira, K., Brunton, S. L., Dawson, S. T., Rowley, C. W., Colonius, T., McKeon, B. J., Schmidt, O. T., Gordeyev, S., Theofilis, V., & Ukeiley, L. S. (2017). Modal analysis of fluid flows: An overview. AIAA Journal, 55(12), 4013–4041. https://doi.org/10.2514/1.J056060 Topal, S., Erkmen, I., & Erkmen, A. M. (2010). A novel map merging methodology for multi-robot systems. World Congress on Engineering and Computer Science I. Tu, J. H., Rowley, C. W., Luchtenburg, D. M., Brunton, S. L., & Kutz, J. N. (2013). On dynamic mode decomposition—Theory and applications. Journal of Computational Dynamics, 66, 1–30. https://doi.org/10.3934/jcd.2014.1.391 Vasilijevic, A., Calado, P., Lopez-Castejon, F., Hayes, D., Stilinovic, N., Nad, D., Mandic, F., Dias, P., Gomes, J., Molina, J. C., Guerrero, A., Gilabert, J., Miskovic, N., Vukic, Z., Sousa, J., & Georgiou, G. (2015). Heterogeneous robotic system for underwater oil spill survey. In MTS/IEEE OCEANS 2015—Genova: Discovering sustainable ocean energy for a new world (pp. 1–7). https://doi.org/10.1109/OCEANS-Genova.2015.7271492 Willcox, K. (2006). Unsteady flow sensing and estimation via the Gappy proper orthogonal decomposition. Computers and Fluids, 35(2), 208–226. https://doi.org/10.1016/j.compfluid.2004.11.006 Xu, Y., Choi, J., & Oh, S. (2011). Mobile sensor network navigation using Gaussian processes with truncated observations. IEEE Transactions on Robotics, 27(6), 1118–1131. https://doi.org/10.1109/TRO.2011.2162766 Zalesak, S. T., Drake, J. F., & Huba, J. D. (1987). Dynamics of three dimensional ionospheric plasma clouds. Physical Review Letters, 58(3), 278–281. https://doi.org/10.1103/PhysRevLett.58.278 Zhang, H., Rowley, C. W., Deem, E. A., & Cattafesta, L. N. (2019). Online dynamic mode decomposition for time-varying systems. SIAM Journal on Applied Dynamical Systems,18(3), 1586–1609. https://doi.org/10.1137/18M1192329, arXiv:1707.02876