A Validated Physical Model For Real-Time Simulation of Soft Robotic Snakes

Gasoto, Renato; Macklin, Miles; Li, Xuan; Sun, Yinan; Erleben, Kenny; Önal, Çağdaş D.; Fu, Jie

doi:10.1109/icra.2019.8794375

Cited by 10 publications

(5 citation statements)

References 27 publications

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“…In [12], we developed a physics-based simulator that models the inflation and deflation of the air chamber and the resulting deformation of the soft bodies with tetrahedral finite elements. The simulator runs in real-time using GPU.…”

Section: System Overviewmentioning

confidence: 99%

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Learning Contact-aware CPG-based Locomotion in a Soft Snake Robot

Li¹,

Önal²,

Fu³

2021

Preprint

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In this paper, we present a model-free learningbased control scheme for the soft snake robot to improve its contact-aware locomotion performance in a cluttered environment. The control scheme includes two cooperative controllers: A bio-inspired controller (C1) that controls both the steering and velocity of the soft snake robot, and an event-triggered regulator (R2) that controls the steering of the snake in anticipation of obstacle contacts and during contact. The inputs from the two controllers are composed as the input to a Matsuoka CPG network to generate smooth and rhythmic actuation inputs to the soft snake. To enable stable and efficient learning with two controllers, we develop a game-theoretic process, fictitious play, to train C1 and R2 with a shared potential-field-based reward function for goal tracking tasks. The proposed approach is tested and evaluated in the simulator and shows significant improvement of locomotion performance in the obstacle-based environment comparing to two baseline controllers.

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Section: System Overviewmentioning

confidence: 99%

“…The learned controller is then applied to the real snake robot. We have shown that with the highfidelity simulator, the controller has a similar performance in both simulations and the real robot [9], [12]. To enable force sensing and obstacle-aided locomotion, we add four point contact force sensors on two sides of each rigid body.…”

Section: System Overviewmentioning

confidence: 99%

Learning Contact-aware CPG-based Locomotion in a Soft Snake Robot

Li¹,

Önal²,

Fu³

2021

Preprint

Self Cite

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“…Soft materials allow soft bioinspired robots to replicate movements of animals in both terrestrial and marine habitats. In the case of the underground environment, some of the most significant prototypes are worm-like robots [2][3][4], caterpillars [5], snakes [6,7], insects [8], and bat wings [9,10]. In the case of the underwater environment, research focuses on the development of prototypes that replicate the movement of fishes [11][12][13][14][15], octopuses [16][17][18], and jellyfishes [19][20][21][22].…”

Section: Soft Underwater Bioinspired Robotsmentioning

confidence: 99%

Soft Underwater Robot Actuated by Shape-Memory Alloys “JellyRobcib” for Path Tracking through Fuzzy Visual Control

2020

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Recent developments in bioinspired technologies combined with the advance of intelligent and soft materials have allowed soft robots to replicate the behavior of different animal species. These devices can perform complicated tasks such as reaching or adapting in constrained and unstructured environments. This article proposes a methodology to develop a soft robot called “JellyRobcib” inspired in morphology and behavior by jellyfish, using shape-memory alloy springs as actuators (as bio-muscles). Such actuators can move the jellyfish both vertically and laterally by applying closed-loop fuzzy and visual controls. Additionally, Computer-Assisted Designs and Computational Fluid Dynamics simulations have been carried out to validate the soft robot model. The results show that the robot movements are very close to the morphological behavior of a real jellyfish regarding the curves of displacements, speeds and accelerations, after performing several experiments for autonomous movement: vertical ascent, lateral movements and trajectory tracking, obtaining an accuracy of ±1479 cm and repeatability of 0.944 for lateral movements for fuzzy visual control. Furthermore, thermal measurements were taken throughout a given path, allowing the generation of temperature gradients within the underwater environment for monitoring purposes.

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“…Nowadays, there are new emerging approaches that merge various techniques such as PCC with FEM [RWGR17]. Others also use FEM but combined in a multiphysics system with spring networks, frictional contact and attachment constraints [GML*19]. And different software tools for FEM simulations are proposed in [GDGS18], or in [HLS*18].…”

Section: State‐of‐the‐artmentioning

confidence: 99%

Modelling the Soft Robot Kyma Based on Real‐Time Finite Element Method

Martín-Barrio

Terrile

Diaz-Carrasco

et al. 2020

Computer Graphics Forum

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Modelling soft robots is a non‐trivial task since their behaviours rely on their morphology, materials and surrounding elements. These robots are very useful to safely interact with their environment and because of their inherent flexibility and adaptability skills. However, they are usually very hard to model because of their intrinsic non‐linearities. This fact presents a unique challenge in the computer graphics and simulation scopes. Current trends in these fields tend to narrow the gap between virtual and real environments. This work will explain a challenging modelling process for a cable‐driven soft robot called Kyma. For this purpose, the real‐time Finite Element Method (FEM) is applied using the Simulation Open Framework Architecture. And two methods are implemented and compared to optimize the model efficiency: a heuristic one and the Model Order Reduction. Both models are also validated with the real robot using a precise motion tracking system. Moreover, an analysis of robot–object interactions is proposed to test the compliance of the presented soft manipulator. As a result, the real‐time FEM emerges as a good solution to accurately and efficiently model the presented robot while also allowing to study the interactions with its environment.

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A Validated Physical Model For Real-Time Simulation of Soft Robotic Snakes

Cited by 10 publications

References 27 publications

Learning Contact-aware CPG-based Locomotion in a Soft Snake Robot

Learning Contact-aware CPG-based Locomotion in a Soft Snake Robot

Soft Underwater Robot Actuated by Shape-Memory Alloys “JellyRobcib” for Path Tracking through Fuzzy Visual Control

Modelling the Soft Robot Kyma Based on Real‐Time Finite Element Method

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