Morphing Structure for Changing Hydrodynamic Characteristics of a Soft Underwater Walking Robot

Ishida, Michael; Drotman, Dylan; Shih, Benjamin; Hermes, Mark; Luhar, Mitul; Tolley, Michael T.

doi:10.1109/lra.2019.2931263

Cited by 50 publications

(41 citation statements)

References 34 publications

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“…The compliance of the soft limbs of the robot also enabled it to squeeze into tight spaces (8). When augmented with an inflatable soft body, we further demonstrated that the robot was able to manipulate the hydrodynamic forces it experienced while walking underwater to improve locomotion for a variety of ambient flow conditions (10).…”

Section: Introductionmentioning

confidence: 79%

Electronics-free pneumatic circuits for controlling soft-legged robots

et al. 2021

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Pneumatically actuated soft robots have recently shown promise for their ability to adapt to their environment. Previously, these robots have been controlled with electromechanical components, such as valves and pumps, that are typically bulky and expensive. Here, we present an approach for controlling the gaits of soft-legged robots using simple pneumatic circuits without any electronic components. This approach produces locomotive gaits using ring oscillators composed of soft valves that generate oscillating signals analogous to biological central pattern generator neural circuits, which are acted upon by pneumatic logic components in response to sensor inputs. Our robot requires only a constant source of pressurized air to power both control and actuation systems. We demonstrate this approach by designing pneumatic control circuits to generate walking gaits for a soft-legged quadruped with three degrees of freedom per leg and to switch between gaits to control the direction of locomotion. In experiments, we controlled a basic walking gait using only three pneumatic memory elements (valves). With two oscillator circuits (seven valves), we were able to improve locomotion speed by 270%. Furthermore, with a pneumatic memory element we designed to mimic a double-pole double-throw switch, we demonstrated a control circuit that allowed the robot to select between gaits for omnidirectional locomotion and to respond to sensor input. This work represents a step toward fully autonomous, electronics-free walking robots for applications including low-cost robotics for entertainment and systems for operation in environments where electronics may not be suitable.

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Section: Introductionmentioning

confidence: 79%

Electronics-free pneumatic circuits for controlling soft-legged robots

et al. 2021

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“…Once the field has reliable solutions for soft robot proprioception, it is conceivable that shape feedback would enable controlled shape-change in robots. Current soft robots do not have much ability to morph into specific configurations, yet even simple shape-change has led to innovative solutions for a wide range of tasks, such as obstacle avoidance (34), rolling locomotion (117), underwater locomotion (118) and camouflage (119). Larger shape-change could result in robots which switch between morphologies and corresponding locomotion gaits on-demand (Fig.…”

Section: Open Questions and Future Directionsmentioning

confidence: 99%

Electronic skins and machine learning for intelligent soft robots

et al. 2020

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Soft robots have garnered interest for real-world applications because of their intrinsic safety embedded at the material level. These robots use deformable materials capable of shape and behavioral changes and allow conformable physical contact for manipulation. Yet, with the introduction of soft and stretchable materials to robotic systems comes a myriad of challenges for sensor integration, including multimodal sensing capable of stretching, embedment of high-resolution but large-area sensor arrays, and sensor fusion with an increasing volume of data. This Review explores the emerging confluence of e-skins and machine learning, with a focus on how roboticists can combine recent developments from the two fields to build autonomous, deployable soft robots, integrated with capabilities for informative touch and proprioception to stand up to the challenges of real-world environments.

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“…To adapt to changing flow conditions underwater, Ishida et al developed a quadruped with a 4-DoF morphing top that could change its drag and lift coefficients to gain assistance from the current when the flow aligned with its direction of motion, and reduce drag when walking against the flow. [76] Similar to the rest of the robots in this category, only a few DoF for shape change were required to allow these robots to continue operation, without searching for complex control strategies to deal with changes in its environment.…”

Section: Shape Changing Robotsmentioning

confidence: 99%

Shape Changing Robots: Bioinspiration, Simulation, and Physical Realization

et al. 2020

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are specialized for a single task, and cannot adapt their body to accomplish additional tasks after manufacture. Moreover, biological bodies are often highly regenerative, and able to repair and reconfigure their large-scale architecture in the face of significant damage or radical changes to their components. [3] For example, salamanders regenerate amputated limbs, [4] and fragments cut from arbitrary portions of planaria flatworms can rebuild (and rescale) their bodies to recover a full, correct anatomy. [3] Remarkably, many of these systems are able to retain information, such as learned memories, despite drastic reconfiguration or total replacement of their brains. [5] In these integrated living systems, intelligence, memory, learning, behavior, and body structure are all intertwined and emerge from the multiscale dynamics of the same robust and highly fault-tolerant medium.Evolution did not result in hard-coded body plans purely determined by genetic factors, but rather produced diverse examples of intelligent self-modifying systems which adapt to numerous extragenomic influences. [6] In this way, biology serves as an important proof-of-principle, and design challenge, for artificial intelligence and shape changing robots. Despite having access to this extensive set of model systems, the realization of general-purpose, adaptive robots has remained elusive. Researchers have proposed modular robots that can be attached to each other to expand functionality, [7] passively conforming universal grippers, [8] reconfigurable robotic skins, [9] self-assembling robot swarms, [10] gait-switching mechanisms [11] and controllers, [12,13] and algorithms that quickly re-adapt to multiple distinct tasks. [14] Such approaches succeed at adaptation but operate under the assumption that the robot's body is only reconfigured or reshaped due to external forces, and do not explore the possibility of synthetic machines that actively grow, regenerate, deform, or otherwise change the resting shape of their constituent components.With the introduction of a conformable gripper by Hirose in 1978, [15] followed by continuum robot arms, [16] silicone grippers, [17] and variable stiffness actuators, [18] robots that can adapt to real-world environments by changing their shape are becoming closer to reality. In particular, the idea of passively conforming around objects during grasping has been quite successful. [17,19,20] Soft robots have shown potential in other applications, including human-robot interaction and exploration, as reviewed by Kim et al., [21] Rus et al., [22] and others. [23,24] For a comprehensive review of the role of deformation in singlefunction soft robots, the reader is referred to Wang et al. [25] One of the key differentiators between biological and artificial systems is the dynamic plasticity of living tissues, enabling adaptation to different environmental conditions, tasks, or damage by reconfiguring physical structure and behavioral control policies. Lack of dynamic plasticity is a significant limitation for a...

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Morphing Structure for Changing Hydrodynamic Characteristics of a Soft Underwater Walking Robot

Cited by 50 publications

References 34 publications

Electronics-free pneumatic circuits for controlling soft-legged robots

Electronics-free pneumatic circuits for controlling soft-legged robots

Electronic skins and machine learning for intelligent soft robots

Shape Changing Robots: Bioinspiration, Simulation, and Physical Realization

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