Toward Shape Optimization of Soft Robots

Morzadec, Thomas; Marcha, Damien; Duriez, Christian

doi:10.1109/robosoft.2019.8722822

Cited by 21 publications

(16 citation statements)

References 25 publications

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“…The reasons for this choice are its good convergence properties and its capacity to avoid local minima. The same algorithm was previously chosen for the design of robotic feet [23] and other integrated design problems [10].…”

Section: A Case Study Description A) Modelmentioning

confidence: 99%

Computational design of energy-efficient legged robots: Optimizing for size and actuators

Fadini

Flayols

Prete

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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This paper presents a computational framework for the design of high-performance legged robotic systems. The framework relies on the concurrent optimization of hardware parameters and control trajectories to find the best robot design for a given task. In particular, we focus on energy efficiency, presenting novel electro-mechanical models to account for the losses of the actuators due to friction and Joule effects. Thanks to a bi-level optimization scheme, featuring a genetic algorithm in the outer loop, our framework can also optimize for the duration of the motion, the actuators, and the size of the robot. We present a novel approach to scale both the actuators and the robot structure in a way that ensures structural integrity by maintaining constant the normalized deflection of the links. We validated our approach by designing a two-joint monoped robot to execute a jumping task. Our simulation results show that our framework can lead to remarkable energy savings (up to 60%) thanks to the concurrent optimization of robot size, motion duration, and actuators.

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Section: A Case Study Description A) Modelmentioning

confidence: 99%

Computational design of energy-efficient legged robots: Optimizing for size and actuators

Fadini

Flayols

Prete

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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“…Moreover, for very complex problems, the differentiability property may be near-impossible to achieve, and one has to resort to derivative-free optimization. While evolutionary algorithms and their siblings require more iterations, they can help to navigate complex design spaces [41] with many local minima and discontinuities [42,43].…”

Section: Alternative Optimization Techniquesmentioning

confidence: 99%

“…Early work by Skouras et al [49] optimized the shape of balloons such that a desired shape was obtained under inflation-there are clear parallels to soft robotic applications. More recent work has looked at the shape optimization of tendon-driven soft robots [38,41] and also the shape optimization of pneumatic actuators [32,35,36,50].…”

Section: Actuation and Controlmentioning

confidence: 99%

Design and Control of Soft Robots Using Differentiable Simulation

2021

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Purpose of Review We discuss the use of differentiable simulation for computational problems in soft robotics. This includes characterizing the mechanical behavior of soft robots, optimally controlling embedded soft actuators or active materials, and estimating the robot's state from readings of embedded sensors. Moreover, we discuss how design optimization can help to optimally place soft actuators and sensors. Recent Findings We expatiate on the adoption of simulation and optimization tools in the process of designing and controlling soft robots. We include a discussion of rigid-flexible systems and the use of differentiable simulation in combination with machine learning. Summary We review the state of the art in the computational modeling of soft robots and provide a summary of the required mathematical tools. We also review several open questions where computation could help to move the field forward, and discuss the role of differentiable simulation in managing the ever-growing design complexity of next-generation soft robots. Keywords Differentiable simulation • Computational design and control • State estimation • Sensor and actuator design This article is part of the Topical Collection on Soft Robotics

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“…PCC models on the other hand are reported by Renda et al (2018) to be inaccurate when the soft device they model is subjected to non-negligible external loads as is typical for FRE fingers. Finite element (FE) models constitute a third popular method in soft robotics and have been shown to be able to produce accurate results for predicting the deformation of grasping soft fingers by Coevoet et al (2019) and conduct shape optimization in Morzadec et al (2019), but as in any model based on FE methods the number of DOFs remains large. Workarounds to deal with this issue have been proposed in the literature such as a procedure where the designer progressively refines the parameter space in Morzadec et al (2019) or algorithmically, as in Goury and Duriez (2018), to reduce the complexity of the model.…”

Section: Introductionmentioning

confidence: 99%

“…Finite element (FE) models constitute a third popular method in soft robotics and have been shown to be able to produce accurate results for predicting the deformation of grasping soft fingers by Coevoet et al (2019) and conduct shape optimization in Morzadec et al (2019), but as in any model based on FE methods the number of DOFs remains large. Workarounds to deal with this issue have been proposed in the literature such as a procedure where the designer progressively refines the parameter space in Morzadec et al (2019) or algorithmically, as in Goury and Duriez (2018), to reduce the complexity of the model. Finally, our model will also be shown to be able to predict with a fair degree of accuracy both the contact forces along the finger and the overall grasp strength, which none of the previous models dealt with.…”

Section: Introductionmentioning

confidence: 99%

Modeling and analysis of soft robotic fingers using the fin ray effect

Shan

Birglen

2020

The International Journal of Robotics Research

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Soft grasping of random objects in unstructured environments has been a research topic of predilection both in academia and in industry because of its complexity but great practical relevance. However, accurate modeling of soft hands and fingers has proven a difficult challenge to tackle. Focusing on this issue, this article presents a detailed mathematical modeling and performance analysis of parallel grippers equipped with soft fingers taking advantage of the fin ray effect (FRE). The FRE, based on biomimetic principles, is most commonly found in the design of grasping soft fingers, but despite their popularity, finding a convenient model to assess the grasp capabilities of these fingers is challenging. This article aims at solving this issue by providing an analytic tool to better understand and ultimately design this type of soft fingers. First, a kinetostatic model of a general multi-crossbeam finger is established. This model will allow for a fast yet accurate estimation of the contact forces generated when the fingers grasp an arbitrarily shaped object. The obtained mathematical model will be subsequently validated by numerically to ensure the estimations of the overall grasp strength and individual contact forces are indeed accurate. Physical experiments conducted with 3D-printed fingers of the most common architecture of FRE fingers will also be presented and shown to support the proposed model. Finally, the impact of the relative stiffness between different areas of the fingers will be evaluated to provide insight into further refinement and optimization of these fingers.

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Toward Shape Optimization of Soft Robots

Cited by 21 publications

References 25 publications

Computational design of energy-efficient legged robots: Optimizing for size and actuators

Computational design of energy-efficient legged robots: Optimizing for size and actuators

Design and Control of Soft Robots Using Differentiable Simulation

Modeling and analysis of soft robotic fingers using the fin ray effect

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