Fabrication, modeling, and control of plush robots

Bern, James M.; Kumagai, Grace; Coros, Stelian

doi:10.1109/iros.2017.8206223

Cited by 30 publications

(14 citation statements)

References 11 publications

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“…Although complex systems such as [16] and [17] have successfully used cable tendon methods, the design and fabrication of cable-driven systems remains highly complex. Since cables require tension, these tendon systems require significant infrastructure, such as rigid pulleys, sheathing, and spindles [18]. Also, a single failure in the transmission line can disable an entire limb, giving cable-based systems similar robustness issues as pneumatic systems.…”

Section: Introductionmentioning

confidence: 99%

Compliant electric actuators based on handed shearing auxetics

Chin

Lipton

MacCurdy

et al. 2018

2018 IEEE International Conference on Soft Robotics (RoboSoft)

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Abstract-In this paper, we explore a new class of electric motor-driven compliant actuators based on handed shearing auxetic cylinders. This technique combines the benefits of compliant bodies from soft robotic actuators with the simplicity of direct coupling to electric motors. We demonstrate the effectiveness of this technique by creating linear actuators, a four degree-of-freedom robotic platform, and a soft robotic gripper. We compare the soft robotic gripper against a state of the art pneumatic soft gripper, finding similar grasping performance in a significantly smaller and more energy-efficient package.

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Section: Introductionmentioning

confidence: 99%

Compliant electric actuators based on handed shearing auxetics

Chin

Lipton

MacCurdy

et al. 2018

2018 IEEE International Conference on Soft Robotics (RoboSoft)

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“…The DDG approach starts with discretization of the smooth system into a mass-spring-type system, while preserving the key geometric properties of actual physical objects; this type of simulation tool is naturally suited to account for contact and collision 23 . In particular, we treat the robot as being composed of multiple slender actuators that can be modeled using elastic rod theories [24][25][26][27][28] . In order to achieve rapid simulation runtimes, we adapt fast and efficient physically based computational techniques that have gained traction within the computer graphics community to model slender structures, e.g., rods [29][30][31] , ribbons 32 , plates 33 , shells 34 , viscous threads 30,35 , and viscous sheets 36 .…”

mentioning

confidence: 99%

Dynamic simulation of articulated soft robots

Huang

Majidi

et al. 2020

Nat Commun

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Soft robots are primarily composed of soft materials that can allow for mechanically robust maneuvers that are not typically possible with conventional rigid robotic systems. However, owing to the current limitations in simulation, design and control of soft robots often involve a painstaking trial. With the ultimate goal of a computational framework for soft robotic engineering, here we introduce a numerical simulation tool for limbed soft robots that draws inspiration from discrete differential geometry based simulation of slender structures. The simulation incorporates an implicit treatment of the elasticity of the limbs, inelastic collision between a soft body and rigid surface, and unilateral contact and Coulombic friction with an uneven surface. The computational efficiency of the numerical method enables it to run faster than real-time on a desktop processor. Our experiments and simulations show quantitative agreement and indicate the potential role of predictive simulations for soft robot design.

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“…Differentiable simulation has seen use in the design and control of tendon-driven soft robots [44], and also in locomotion and manipulation tasks where contact with the environment must be considered [45 •, 46]. Rod models have seen widespread use in soft body modeling.…”

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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Fabrication, modeling, and control of plush robots

Cited by 30 publications

References 11 publications

Compliant electric actuators based on handed shearing auxetics

Compliant electric actuators based on handed shearing auxetics

Dynamic simulation of articulated soft robots

Design and Control of Soft Robots Using Differentiable Simulation

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