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As a result, the state vectors x SS and x DS are x S S ≔ [ t S S − , v c , x , v c , x − , v c , z , p c , z , p F F , x ] T and x D S ≔ [ t D S − , v c , x , v c , z , p c , z , p F F , x ] T, where the ‘-’ superscript stands for the value of the previous phase. Considering the analysis in Section 3.2, the result is an acceptable choice and consistent with the author’s experience. 4.2 Function Approximation Schreuders, E. H. et al. Colorectal cancer screening: a global overview of existing programmes. Gut 64, 1637–1649, https://doi.org/10.1136/gutjnl-2014-309086 (2015).

BALLU2: A Safe and Affordable Buoyancy Assisted Biped

Isayev, A. I. & Hieber, C. A. Toward a viscoelastic modelling of the injection molding process. Rheol. Acta 19, 168–182, https://doi.org/10.1007/BF01521928 (1980). Ghassemi and Hong (2016) introduced the first iteration of BALLU to verify the concept of buoyancy assisted legged robot, but it was limited to teleoperation using radio control signals. In this paper, the next iteration, BALLU2, which is the first implementation that can walk by algorithms, is presented with details on its design and controller. The contributions of this work are as follows: The controller was tested both on simulation and on real-world hardware, and its performances were accessed based on the resulting trajectories and phase plots. In both simulation and hardware experiments, the controller generated stable limit cycles after around 10s. In particular, the controller worked on the hardware without additional transfer learning. In addition, the controller was tested under unseen conditions during the training condition. The results showed that the robustness of the controller but also suggested that the proposed method was able to extract the key variables that govern BALLU’s walking from the data.Ranzani, T., Gerboni, G., Cianchetti, M. & Menciassi, A. A bioinspired soft manipulator for minimally invasive surgery. Bioinspiration & Biomimetics 10, 035008, https://doi.org/10.1088/1748-3190/10/3/035008 (2015). Horton, K. M., Corl, F. M. & Fishman, E. K. Ct evaluation of the colon: Inflammatory disease. RadioGraphics 20, 399–418, https://doi.org/10.1148/radiographics.20.2.g00mc15399 (2000). We trained a planar data-driven walking controller with artificial neural networks using the definition of the state and a set of expert data.

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Miyamae H, Uchida M (2019) Constructing a control system using a time-state control form for a fish-type balloon robot. Artif Life Robot 25:316–321 L.M. conceived the robot, designed the experiments, and wrote the main manuscript text. E.C. and L.M. fabricated the robot and conducted the experimental characterization. L.M. and E.C. prepared all figures. All authors reviewed the manuscript and analysed the results. Corresponding authorBecause of the complexity and uncertainty of the system, rather than conducting formal nonlinear system analysis, BALLU’s walking performance is first qualitatively assessed by analyzing the pelvis’ trajectories and phase plot. Kozarek, R. A., Earnest, D. L., Silverstein, M. E. & Smith, R. G. Air-pressure-induced colon injury during diagnostic colonoscopy. Gastroenterology 78, 7–14, https://doi.org/10.1016/0016-5085(80)90185-7 (1980).