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HC is the main contributor that designed the new iteration of BALLU, created the data-driven controller, conducted experiments, and wrote the manuscript. MA participated in devising new types of controllers for BALLU and writing the manuscript. DN and HN contributed to manufacturing and maintaining BALLU. DH is the inventor and the PI for BALLU, guided the project as the advisor, and contributed to the review of the paper. Funding For the design parameters and the knee joint displacement θ within the joint limits, a range of spring constant κ can be obtained from Eq. 8. Among satisfying κ, we prefer to choose a relatively low stiffness. It is because, when the spring constant takes a lower value, the dynamics change more drastically between a single support phase and a double support phase, and the controller would have more options to control its motion by adjusting each phase length. Through a handful of empirical tests, a torsion spring with 0.1409N mm/deg with 135° was chosen. 2.1.5 Feet
BALLU2: A Safe and Affordable Buoyancy Assisted Biped
Future work includes expanding the proposed approach to build a walking controller in 3D space as well as an onboard state estimator with more sensors so that walking can be achieved independently. We are also looking forward to applying the proposed statistical method to other nontypical robots and build controllers based on the states extracted from data. Data Availability StatementIn practice, as injecting the same amount of helium every time to keep the body afloat is difficult, a generous amount of helium is initially injected, and counterweights are attached to the body to adjust the net upward force. This is done by calibrating the robot’s normal weight on a scale, which is empirically chosen to be 55 gf in the presented version of BALLU. Such a choice allows the robot to stay vertically upright when in a double support phase, but sink when in a single support phase, which will be important in the subsequent locomotion approach. 2.1.2 Pelvis Link
balloon (SPID) for - Nature A Soft Pneumatic Inchworm Double balloon (SPID) for - Nature
We presented a statistical method that can extract a state vector from data collected from the highly nonlinear dynamics, using Spearman Correlation Coefficient. The state vector is low dimensional yet contains essential information of the high dimensional system. The magnitude of the balloons’ net buoyancy, which is the difference between the buoyancy of the helium and the weight of the balloons, must be smaller than the total body weight to prevent the robot from floating in the air. This net buoyancy is controlled to support most of the body weight, with the normal forces at each foot supporting the rest.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). Hybrid balloon: A hybrid balloon robot is a combination of hot air and a gas balloon. These are relatively new technology and are typically helpful for long-duration flights. In BALLU2, to enhance BALLU’s functionality, several changes have been made to the BALLU1’s design. The new iteration of BALLU can primarily be broken up into four components: the balloons, the pelvis link, the leg, the knee joint, and the foot. 2.1.1 Balloons The current system is set up for easy and modular development, as various processes are concurrently running on board, including the motor controller and the communication and messaging module. Shared memory is leveraged to share data between processes. Python is primarily used for simplicity, while C++ is used for low-level modules. 2.3 Simulation Environment 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
Balloon Robot Charms at California Tech Fair Bipedal Balloon Robot Charms at California Tech Fair
Wang, K. et al. Full-driving soft robotic colonoscope in compliant colon tissue. Journal of Medical Engineering & Technology 41, 1–8, https://doi.org/10.1080/03091902.2017.1394387 (2017). The first set of simulations make changes in mass properties. With heavier feet or pelvis, the body would be easier to sink down. First, the pelvis mass is increased by 6.4% (from 31.2 to 33.2 g), and secondly, the feet masses are increased by 16.5% (from 24.2 to 28.2 g), respectively. The second set of modifications is the change in commanded velocity, and two simulations are conducted in a slower velocity and a higher velocity. 5.2 Hardware Verification Maple, J. T. et al. Methods of luminal distention for colonoscopy. Gastrointestinal endoscopy 77, 519–525, https://doi.org/10.1016/j.gie.2012.09.025 (2013). Yamakawa S (2013) Stabilization condition of chained system based on time-based state control. Trans Soc Instrum Control Eng 49(5):499–504 The original contributions presented in the study are included in the article material, further inquiries can be directed to the corresponding authors. Author ContributionsDekker, E. & Rex, D. K. Advances in crc prevention: Screening and surveillance. Gastroenterology 154, 1970–1984, https://doi.org/10.1053/j.gastro.2018.01.069 (2018). One abstract possibility for autonomous vehicles is one that does not need any kind of connection with any external human or AI system, can completely fly and navigate on its own, and contains programmed free will. While the definition of “autonomous” does limit it to be called so, it’s still possible. In the future, it is possible that autonomous flying robots will be able to fly in formation, collaborate on tasks, and autonomously make decisions in real-time. 4) Drones Hu FJ, Lin JW, Gu HJ (2019) Dynamic linear predictive optimization of flexible robot profiling MFA model. J Sens 2019:1–9
Balloon Puncher Robots from Silverlit Toys, Battle Until a
Breedveld, P. Development of a rolling stent endoscope. In The First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, 2006. BioRob 2006, 921–926, https://doi.org/10.1109/BIOROB.2006.1639209 (2006).Liu Y, Dong HB, Lohse N, Petrovic S (2016) A multi-objective genetic algorithm for optimization of energy consumption and shop floor production performance. Int J Prod Econ 179:259–272 Saad A, Khan SA, Mahmood A (2018) A multi-objective evolutionary artificial bee colony algorithm for optimizing network topology design. Swarm Evolut Comput 38:187–201 Hosokawa, D., Ishikawa, T., Morikawa, H., Imai, Y. & Yamaguchi, T. Development of a biologically inspired locomotion system for a capsule endoscope. The International Journal of Medical Robotics and Computer Assisted Surgery 5, 471–478, https://doi.org/10.1002/rcs.284 (2009).
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