Soft Passive Swimmer Optimization: From Simulation to Reality Using Data-Driven Transformation

Obayashi, Nana; Bosio, Carlo; Hughes, Josie

doi:10.1109/robosoft54090.2022.9762148

Cited by 11 publications

(9 citation statements)

References 11 publications

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“…In previous works [25], we reported on the fluid propulsion generated by an oscillating tentacle structure. Similar designs have been proposed in different scales, ranging from microrobotics [9], to large scale robots [12].…”

Section: Optimization Methodsmentioning

confidence: 99%

An Experimental Validation of the Polynomial Curvature Model: Identification and Optimal Control of a Soft Underwater Tentacle

Stella

Obayashi

Santina

et al. 2022

IEEE Robot. Autom. Lett.

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The control possibilities for soft robots have long been hindered by the lack of accurate yet computationally treatable dynamic models of soft structures. Polynomial curvature models propose a solution to this quest for continuum slender structures. Nevertheless, the results produced with this class of models have been so far essentially theoretical. With the present work, we aim to provide a much-needed experimental validation to these recent theories. To this end, we focus on soft tentacles immersed in water. First, we propose an extension of the affine curvature model to underwater structures, considering the drag forces arising from the fluid-solid interaction. Then, we extensively test the model's capability to describe the system behavior across several shapes and working conditions. Finally, we validate model-based control policies, proposing and solving an optimal control problem for directional underwater swimming. Using the model we show an average increase of more than 3.5 times the swimming speed of a sinusoidal baseline controller, with some tentacles showing an improvement in excess of 5.5 times the baseline.

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Section: Optimization Methodsmentioning

confidence: 99%

An Experimental Validation of the Polynomial Curvature Model: Identification and Optimal Control of a Soft Underwater Tentacle

Stella

Obayashi

Santina

et al. 2022

IEEE Robot. Autom. Lett.

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“…As a fourth degree of classification, the color of the border indicates whether the design process was application oriented,-in red -or exploration oriented-in yellow. In the latter, the goal of building a robot with specific capabilities spurs the development of novel technologies with any of the pipelines presented in Figure 2 Frontiers in Robotics and AI frontiersin.org simulation, tuned with feedback from a few real world experiments, may lead to better results for both classical rigid robots (Truong et al, 2022) and soft systems (Obayashi et al, 2022a). Another approach used to avoid the simulation to reality gap is performing the evolution of the control strategy (Veenstra et al, 2018) and of the robot morphology directly on the real system.…”

Section: Figurementioning

confidence: 99%

“…In the latter, the goal of building a robot with specific capabilities spurs the development of novel technologies with any of the pipelines presented in Figure 2 . a-( Cheney et al, 2014 ), b-( Obayashi et al, 2022b ), c-( Amend et al, 2012 ), d-( Kriegman et al, 2020 ), e-( Hawkes et al, 2017 ), f-( Cheng et al, 2012 ), g-( Obayashi et al, 2022a ), h-( Katzschmann et al, 2018 ).…”

Section: Approaches To Soft Robot Designmentioning

confidence: 99%

“…This is mainly due to the difficulty of the simulation to capture the nonlinear material characteristics. Recently it has been shown that using a low fidelity simulation, tuned with feedback from a few real world experiments, may lead to better results for both classical rigid robots ( Truong et al, 2022 ) and soft systems ( Obayashi et al, 2022a ). Another approach used to avoid the simulation to reality gap is performing the evolution of the control strategy ( Veenstra et al, 2018 ) and of the robot morphology directly on the real system.…”

Section: Approaches To Soft Robot Designmentioning

confidence: 99%

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The science of soft robot design: A review of motivations, methods and enabling technologies

Stella

Hughes

2023

Front. Robot. AI

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Novel technologies, fabrication methods, controllers and computational methods are rapidly advancing the capabilities of soft robotics. This is creating the need for design techniques and methodologies that are suited for the multi-disciplinary nature of soft robotics. These are needed to provide a formalized and scientific approach to design. In this paper, we formalize the scientific questions driving soft robotic design; what motivates the design of soft robots, and what are the fundamental challenges when designing soft robots? We review current methods and approaches to soft robot design including bio-inspired design, computational design and human-driven design, and highlight the implications that each design methods has on the resulting soft robotic systems. To conclude, we provide an analysis of emerging methods which could assist robot design, and we present a review some of the necessary technologies that may enable these approaches.

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“…The morphology and structure of systems, whether they are artificial or biological, play a crucial role in determining their behavior and functionality [1][2][3]. In biological systems, morphogenesis refers to the process by which the morphology of an organ or organism emerges [4].…”

Section: Introductionmentioning

confidence: 99%

Environmentally-Driven Morphogenesis of Reconfigurable Floating Robots using Markov Models

Obayashi

Meyer

Junge

et al. 2023

Preprint

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The morphology and structure plays a crucial role in shaping the behavior and functionality of both biological and robotic systems.In this work, we are inspired by morphogenesis, a fundamental biological process which encompasses the emergence of organs and organism morphology. It is driven by both internal and environmental factors and profoundly influences cellular behavior during development. By studying the environmental-driven properties that give rise to specific cellular morphologies, we investigate how control of the environment can be harnessed to trigger structural changes. Furthermore, we explore the potential application of these principles to inspire the development of robots with predefined morphologies. However, this endeavor is inherently stochastic and challenging to simulate accurately. To address this complexity, we propose leveraging a Markov decision process-inspired controller, guided by a Markov model. Using this approach, we can achieve decentralized control instead of localized control and design scalable robotic systems that can reconfigure their morphologies and display different motion characteristics in response to environmental cues.

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Soft Passive Swimmer Optimization: From Simulation to Reality Using Data-Driven Transformation

Cited by 11 publications

References 11 publications

An Experimental Validation of the Polynomial Curvature Model: Identification and Optimal Control of a Soft Underwater Tentacle

An Experimental Validation of the Polynomial Curvature Model: Identification and Optimal Control of a Soft Underwater Tentacle

The science of soft robot design: A review of motivations, methods and enabling technologies

Environmentally-Driven Morphogenesis of Reconfigurable Floating Robots using Markov Models

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