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      Soft Pneumatic Actuators: A Review of Design, Fabrication, Modeling, Sensing, Control and Applications

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          Deep learning in neural networks: An overview

          In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
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            An integrated design and fabrication strategy for entirely soft, autonomous robots.

            Soft robots possess many attributes that are difficult, if not impossible, to achieve with conventional robots composed of rigid materials. Yet, despite recent advances, soft robots must still be tethered to hard robotic control systems and power sources. New strategies for creating completely soft robots, including soft analogues of these crucial components, are needed to realize their full potential. Here we report the untethered operation of a robot composed solely of soft materials. The robot is controlled with microfluidic logic that autonomously regulates fluid flow and, hence, catalytic decomposition of an on-board monopropellant fuel supply. Gas generated from the fuel decomposition inflates fluidic networks downstream of the reaction sites, resulting in actuation. The body and microfluidic logic of the robot are fabricated using moulding and soft lithography, respectively, and the pneumatic actuator networks, on-board fuel reservoirs and catalytic reaction chambers needed for movement are patterned within the body via a multi-material, embedded 3D printing technique. The fluidic and elastomeric architectures required for function span several orders of magnitude from the microscale to the macroscale. Our integrated design and rapid fabrication approach enables the programmable assembly of multiple materials within this architecture, laying the foundation for completely soft, autonomous robots.
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              Multigait soft robot.

              This manuscript describes a unique class of locomotive robot: A soft robot, composed exclusively of soft materials (elastomeric polymers), which is inspired by animals (e.g., squid, starfish, worms) that do not have hard internal skeletons. Soft lithography was used to fabricate a pneumatically actuated robot capable of sophisticated locomotion (e.g., fluid movement of limbs and multiple gaits). This robot is quadrupedal; it uses no sensors, only five actuators, and a simple pneumatic valving system that operates at low pressures (< 10 psi). A combination of crawling and undulation gaits allowed this robot to navigate a difficult obstacle. This demonstration illustrates an advantage of soft robotics: They are systems in which simple types of actuation produce complex motion.
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                Journal
                IEEE Access
                IEEE Access
                Institute of Electrical and Electronics Engineers (IEEE)
                2169-3536
                2022
                2022
                : 10
                : 59442-59485
                Affiliations
                [1 ]Precision Mechatronics Laboratory, School of Engineering, The University of Newcastle, Callaghan, NSW, Australia
                [2 ]Faculty of Engineering and Computer Science, University of Wollongong in Dubai, Dubai, United Arab Emirates
                [3 ]School of Engineering, Deakin University, Geelong, VIC, Australia
                [4 ]Data61, CSIRO, Pullenvale, QLD, Australia
                [5 ]Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hong Kong
                [6 ]Department of Engineering, School of Science and Technology, Nottingham Trent University, Nottingham, U.K.
                Article
                10.1109/ACCESS.2022.3179589
                a7865e26-504c-4a4b-9220-8dff8d0b192b
                © 2022

                https://creativecommons.org/licenses/by/4.0/legalcode

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